54 Kubeflow Jobs

About this role: Lead Software Engineer (AI) position having experience in classic and generative AI techniques, and responsible for design, implementation, and support of Python based applications to help fulfill our Research & Consulting Delivery strategy. What youll do: Deliver client engagements that use AI rapidly, on the order of a few weeks Stay on top of current tools, techniques, and frameworks to be able to use and advise clients on them Build proofs of concept rapidly, to learn and adapt to changing market needs Support building internal applications for use by associates to improve productivity What you’ll need: 6-8 years of experience in classic AI techniques and at least 1.5 years in generative AI techniques. Demonstrated ability to run short development cycles and solid grasp of building software in a collaborative team setting. Must have: Experience building applications for knowledge search and summarization, frameworks to evaluate and compare performance of different GenAI techniques, measuring and improving accuracy and helpfulness of generative responses, implementing observability. Experience with agentic AI frameworks, RAG, embedding models, vector DBs Experience working with Python libraries like Pandas, Scikit-Learn, Numpy, and Scipy is required. Experience deploying applications to cloud platforms such as Azure and AWS. Solid grasp of building software in a collaborative team setting - use of agile scrum and tools like Jira / GitHub. Nice to have: Experience in finetuning Language models. Familiarity with AWS Bedrock / Azure AI / Databricks Services. Experience in Machine learning models and techniques like NLP, BERT, Transformers, Deep learning. Experience in MLOps Frameworks like Kubeflow, MLFlow, DataRobot, Airflow etc., Experience building scalable data models and performing complex relational databases queries using SQL (Oracle, MySQL, PostgreSQL). Who you are: Excellent written, verbal, and interpersonal communication skills with the ability to present technical information in a clear and concise manner to IT Leaders and business stakeholders. Effective time management skills and ability to meet deadlines. Excellent communications skills interacting with technical and business audiences. Excellent organization, multitasking, and prioritization skills. Must possess a willingness and aptitude to embrace new technologies/ideas and master concepts rapidly. Intellectual curiosity, passion for technology and keeping up with new trends. Delivering project work on-time within budget with high quality. Demonstrated ability to run short development cycle.

My profile - linkedin.com/in/yashsharma1608 Position : AI Architect ( Gen AI ) Experience : 8 - 10 years Notice Period : Immediate to 15 days. Budget upto - 45 to 50 LPA Location : Bangalore. Note : - (any developer with minimum 3 to 4 years into AI), SaaS company mandatory. Product Based company Mandatory Discuss the feasibility of AI/ML use cases along with architectural design with business teams and translate the vision of business leaders into realistic technical implementation Play a key role in defining the AI architecture and selecting appropriate technologies from a pool of open-source and commercial offerings Design and implement robust ML infrastructure and deployment pipelines Establish comprehensive MLOps practices for model training, versioning, and deployment Lead the development of HR-specialized language models (SLMs) Implement model monitoring, observability, and performance optimization frameworks Develop and execute fine-tuning strategies for large language models Create and maintain data quality assessment and validation processes Design model versioning systems and A/B testing frameworks Define technical standards and best practices for AI development Optimize infrastructure for cost, performance, and scalability Required Qualifications 7+ years of experience in ML/AI engineering or related technical roles 3+ years of hands-on experience with MLOps and production ML systems Demonstrated expertise in fine-tuning and adapting foundation models Strong knowledge of model serving infrastructure and orchestration Proficiency with MLOps tools (MLflow, Kubeflow, Weights & Biases, etc.) Experience implementing model versioning and A/B testing frameworks Strong background in data quality methodologies for ML training Proficiency in Python and ML frameworks (PyTorch, TensorFlow, Hugging Face) Experience with cloud-based ML platforms (AWS, Azure, Google Cloud) Proven track record of deploying ML models at scale Preferred Qualifications Experience developing AI applications for enterprise software domains Knowledge of distributed training techniques and infrastructure Experience with retrieval-augmented generation (RAG) systems Familiarity with vector databases (Pinecone, Weaviate, Milvus) Understanding of responsible AI practices and bias mitigation Bachelor's or Master's degree in Computer Science, Machine Learning, or related field What We Offer Opportunity to shape AI strategy for a fast-growing HR technology leader Collaborative environment focused on innovation and impact Competitive compensation package Professional development opportunities Flexible work arrangements Qualified candidates who are passionate about applying cutting-edge AI to transform HR technology are encouraged to apply

Location: Bangalore Experience: 5 - 7 Years Notice Period: Immediate to 15 Days Overview: We are seeking a skilled Data Engineer to design, build, and maintain scalable data pipelines that support Machine Learning (ML) and analytics workloads. This role requires close collaboration with ML teams, ensuring high data quality, system reliability, and performance across complex data environments. Key Responsibilities: Collaborate with ML teams to deploy, monitor, and optimize models in production. Build and maintain scalable, high-performance data pipelines and infrastructure. Implement statistical analysis and experimental design to validate data and model performance. Ensure data quality, governance, and system efficiency in large-scale architectures. Monitor, troubleshoot, and optimize data systems and workflows. Required Skills: 56 years of experience in data engineering with a strong background in Python , SQL/NoSQL , and Apache Spark . Solid understanding of data warehousing , data modeling , and architecture principles . Experience with cloud platforms such as AWS , GCP , or Azure . Hands-on experience with ML pipeline tools (e.g., MLflow , Kubeflow ). Familiarity with search , recommendation systems , or NLP technologies . Strong grasp of statistics and experimental design in ML contexts. Proactive problem-solving skills and the ability to work independently or in teams.

Senior xOps Specialist AIOps, MLOps & DataOps Architect Location: Chennai, Pune Employment Type: Fulltime - Hybrid Experience Required: 12-15 years Job Summary: We are seeking a Senior xOps Specialist to architect, implement, and optimize AI-driven operational frameworks across AIOps, MLOps, and DataOps. The ideal candidate will design and enhance intelligent automation, predictive analytics, and resilient pipelines for large-scale data engineering, AI/ML deployments, and IT operations. This role requires deep expertise in AI/ML automation, data-driven DevOps strategies, observability frameworks, and cloud-native orchestration. Key Responsibilities – Design & Architecture AIOps: AI-Driven IT Operations & Automation Architect AI-powered observability platforms, ensuring predictive incident detection and autonomous IT operations. Implement AI-driven root cause analysis (RCA) for proactive issue resolution and performance optimization. Design self-healing infrastructures leveraging machine learning models for anomaly detection and remediation workflows. Establish event-driven automation strategies, enabling autonomous infrastructure scaling and resilience engineering. MLOps: Machine Learning Lifecycle Optimization Architect end-to-end MLOps pipelines, ensuring automated model training, validation, deployment, and monitoring. Design CI/CD pipelines for ML models, embedding drift detection, continuous optimization, and model explainability. Implement feature engineering pipelines, leveraging data versioning, reproducibility, and intelligent retraining techniques. Ensure secure and scalable AI/ML environments, optimizing GPU-accelerated processing and cloud-native model serving. DataOps: Scalable Data Engineering & Pipelines Architect data processing frameworks, ensuring high-performance, real-time ingestion, transformation, and analytics. Build data observability platforms, enabling automated anomaly detection, data lineage tracking, and schema evolution. Design self-optimizing ETL pipelines, leveraging AI-driven workflows for data enrichment and transformation. Implement governance frameworks, ensuring data quality, security, and compliance with enterprise standards. Automation & API Integration Develop Python or Go-based automation scripts for AI model orchestration, data pipeline optimization, and IT workflows. Architect event-driven xOps frameworks, enabling intelligent orchestration for real-time workload management. Implement AI-powered recommendations, optimizing resource allocation, cost efficiency, and performance benchmarking. Cloud-Native & DevOps Integration Embed AI/ML observability principles within DevOps pipelines, ensuring continuous monitoring and retraining cycles. Architect cloud-native solutions optimized for Kubernetes, containerized environments, and scalable AI workloads. Establish AIOps-driven cloud infrastructure strategies, automating incident response and operational intelligence. Qualifications & Skills – xOps Expertise Deep expertise in AIOps, MLOps, and DataOps, designing AI-driven operational frameworks. Proficiency in automation scripting, leveraging Python, Go, and AI/ML orchestration tools. Strong knowledge of AI observability, ensuring resilient IT operations and predictive analytics. Extensive experience in cloud-native architectures, Kubernetes orchestration, and serverless AI workloads. Ability to troubleshoot complex AI/ML pipelines, ensuring optimal model performance and data integrity. Preferred Certifications (Optional): AWS Certified Machine Learning Specialist Google Cloud Professional Data Engineer Kubernetes Certified Administrator (CKA) DevOps Automation & AIOps Certification

Expected Notice Period : 15 Days Shift : (GMT+05:30) Asia/Kolkata (IST) Opportunity Type : Office (Hyderabad) Placement Type : Full Time Permanent position(Payroll and Compliance to be managed by: InfraCloud Technologies Pvt Ltd) (*Note: This is a requirement for one of Uplers' client - IF) What do you need for this opportunity? Must have skills required: Banking, Fintech, Product Engineering background, Python, FastAPI, Django, MLFlow, feast, Kubeflow, NumPy, pandas, Big Data IF is Looking for: Product Engineer Location: N arsingi, Hyderabad 5 days of work from the Office Client is a Payment gateway processing company Interview Process: Screening round with InfraCloud, followed by a second round with our Director of Engineering. We share the profile with the client, and they take one/two interviews About the Project We are building a high-performance machine learning engineering platform that powers scalable, data-driven solutions for enterprise environments. Your expertise in Python, performance optimization, and ML tooling will play a key role in shaping intelligent systems for data science and analytics use cases. Experience with MLOps, SaaS products, or big data environments will be a strong plus. Role and Responsibilities Design, build, and optimize components of the ML engineering pipeline for scalability and performance. Work closely with data scientists and platform engineers to enable seamless deployment and monitoring of ML models. Implement robust workflows using modern ML tooling such as Feast, Kubeflow, and MLflow. Collaborate with cross-functional teams to design and scale end-to-end ML services across a cloud-native infrastructure. Leverage frameworks like NumPy, Pandas, and distributed compute environments to manage large-scale data transformations. Continuously improve model deployment pipelines for reliability, monitoring, and automation. Requirements 5+ years of hands-on experience in Python programming with a strong focus on performance tuning and optimization. Solid knowledge of ML engineering principles and deployment best practices. Experience with Feast, Kubeflow, MLflow, or similar tools. Deep understanding of NumPy, Pandas, and data processing workflows. Exposure to big data environments and a good grasp of data science model workflows. Strong analytical and problem-solving skills with attention to detail. Comfortable working in fast-paced, agile environments with frequent cross-functional collaboration. Excellent communication and collaboration skills. Nice to Have Experience deploying ML workloads in public cloud environments (AWS, GCP, or Azure). Familiarity with containerization technologies like Docker and orchestration using Kubernetes. Exposure to CI/CD pipelines, serverless frameworks, and modern cloud-native stacks. Understanding of data protection, governance, or security aspects in ML pipelines. Experience Required: 5+ years

Shift: (GMT+05:30) Asia/Kolkata (IST) What do you need for this opportunity? Must have skills required: Machine Learning, ML, ml architectures and lifecycle, Airflow, Kubeflow, MLFlow, Spark, Kubernetes, Docker, Python, SQL, machine learning platforms, BigQuery, GCS, Dataproc, AI Platform, Search Ranking, Deep Learning, Deep Learning Frameworks, PyTorch, TensorFlow About the job Candidates for this position are preferred to be based in Bangalore, India and will be expected to comply with their team's hybrid work schedule requirements. Who We Are Wayfairs Advertising business is rapidly expanding, adding hundreds of millions of dollars in profits to Wayfair. We are building Sponsored Products, Display & Video Ad offerings that cater to a variety of Advertiser goals while showing highly relevant and engaging Ads to millions of customers. We are evolving our Ads Platform to empower advertisers across all sophistication levels to grow their business on Wayfair at a strong, positive ROI and are leveraging state of the art Machine Learning techniques. What youll do Provide technical leadership in the development of an automated and intelligent advertising system by advancing the state-of-the-art in machine learning techniques to support recommendations for Ads campaigns and other optimizations. Design, build, deploy and refine extensible, reusable, large-scale, and real-world platforms that optimize our ads experience. Work cross-functionally with commercial stakeholders to understand business problems or opportunities and develop appropriately scoped machine learning solutions Collaborate closely with various engineering, infrastructure, and machine learning platform teams to ensure adoption of best-practices in how we build and deploy scalable machine learning services Identify new opportunities and insights from the data (where can the models be improved? What is the projected ROI of a proposed modification?) Research new developments in advertising, sort and recommendations research and open-source packages, and incorporate them into our internal packages and systems. Be obsessed with the customer and maintain a customer-centric lens in how we frame, approach, and ultimately solve every problem we work on. We Are a Match Because You Have: Bachelor's or Masters degree in Computer Science, Mathematics, Statistics, or related field. 6-9 years of industry experience in advanced machine learning and statistical modeling, including hands-on designing and building production models at scale. Strong theoretical understanding of statistical models such as regression, clustering and machine learning algorithms such as decision trees, neural networks, etc. Familiarity with machine learning model development frameworks, machine learning orchestration and pipelines with experience in either Airflow, Kubeflow or MLFlow as well as Spark, Kubernetes, Docker, Python, and SQL. Proficiency in Python or one other high-level programming language Solid hands-on expertise deploying machine learning solutions into production Strong written and verbal communication skills, ability to synthesize conclusions for non-experts, and overall bias towards simplicity Nice to have Familiarity with Machine Learning platforms offered by Google Cloud and how to implement them on a large scale (e.g. BigQuery, GCS, Dataproc, AI Notebooks). Experience in computational advertising, bidding algorithms, or search ranking Experience with deep learning frameworks like PyTorch, Tensorflow, etc.

Key Responsibilities 1. Designing technology systems: Plan and design the structure of technology solutions, and work with design and development teams to assist with the process. 2. Communicating: Communicate system requirements to software development teams, and explain plans to developers and designers. They also communicate the value of a solution to stakeholders and clients. 3. Managing Stakeholders: Work with clients and stakeholders to understand their vision for the systems. Should also manage stakeholder expectations. 4. Architectural Oversight: Develop and implement robust architectures for AI/ML and data science solutions, ensuring scalability, security, and performance. Oversee architecture for data-driven web applications and data science projects, providing guidance on best practices in data processing, model deployment, and end-to-end workflows. 5. Problem Solving: Identify and troubleshoot technical problems in existing or new systems. Assist with solving technical problems when they arise. 6. Ensuring Quality: Ensure if systems meet security and quality standards. Monitor systems to ensure they meet both user needs and business goals. 7. Project management: Break down project requirements into manageable pieces of work, and organise the workloads of technical teams. 8. Tool & Framework Expertise: Utilise relevant tools and technologies, including but not limited to LLMs, TensorFlow, PyTorch, Apache Spark, cloud platforms (AWS, Azure, GCP), Web App development frameworks and DevOps practices. 9. Continuous Improvement: Stay current on emerging technologies and methods in AI, ML, data science, and web applications, bringing insights back to the team to foster continuous improvement. Technical Skills 1. Proficiency in AI/ML frameworks such as TensorFlow, PyTorch, Keras, and scikit-learn for developing machine learning and deep learning models. 2. Knowledge or experience working with self-hosted or managed LLMs. 3. Knowledge or experience with NLP tools and libraries (e.g., SpaCy, NLTK, Hugging Face Transformers) and familiarity with Computer Vision frameworks like OpenCV and related libraries for image processing and object recognition. 4. Experience or knowledge in back-end frameworks (e.g., Django, Spring Boot, Node.js, Express etc.) and building RESTful and GraphQL APIs. 5. Familiarity with microservices, serverless, and event-driven architectures. Strong understanding of design patterns (e.g., Factory, Singleton, Observer) to ensure code scalability and reusability. 6. Proficiency in modern front-end frameworks such as React, Angular, or Vue.js, with an understanding of responsive design, UX/UI principles, and state management (e.g., Redux) 7. In-depth knowledge of SQL and NoSQL databases (e.g., PostgreSQL, MongoDB, Cassandra), as well as caching solutions (e.g., Redis, Memcached). 8. Expertise in tools such as Apache Spark, Hadoop, Pandas, and Dask for large-scale data processing. 9. Understanding of data warehouses and ETL tools (e.g., Snowflake, BigQuery, Redshift, Airflow) to manage large datasets. 10. Familiarity with visualisation tools (e.g., Tableau, Power BI, Plotly) for building dashboards and conveying insights. 11. Knowledge of deploying models with TensorFlow Serving, Flask, FastAPI, or cloud-native services (e.g., AWS SageMaker, Google AI Platform). 12. Familiarity with MLOps tools and practices for versioning, monitoring, and scaling models (e.g., MLflow, Kubeflow, TFX). 13. Knowledge or experience in CI/CD, IaC and Cloud Native toolchains. 14. Understanding of security principles, including firewalls, VPC, IAM, and TLS/SSL for secure communication. 15. Knowledge of API Gateway, service mesh (e.g., Istio), and NGINX for API security, rate limiting, and traffic management.

Job Title: Data/ML Platform Engineer Location: Gurgaon, Pune, Bangalore, Chennai, Bhopal, Jaipur, Hyderabad (Work from office) Notice Period: ImmediateiSource Services is hiring for one of their client for the position of Data/ML Platform Engineer. As a Data Engineer you will be relied on to independently develop and deliver high-quality features for our new ML Platform, refactor and translate our data products and finish various tasks to a high standard. Youll be part of the Data Foundation Team, which focuses on creating and maintaining the Data Platform for Marktplaats. 5 years of hands-on experience in using Python, Spark,Sql. Experienced in AWS Cloud usage and management. Experience with Databricks (Lakehouse, ML, Unity Catalog, MLflow). Experience using various ML models and frameworks such as XGBoost, Lightgbm, Torch. Experience with orchestrators such as Airflow and Kubeflow. Familiarity with containerization and orchestration technologies (e.g., Docker, Kubernetes). Fundamental understanding of Parquet, Delta Lake and other data file formats. Proficiency on an IaC tool such as Terraform, CDK or CloudFormation. Strong written and verbal English communication skill and proficient in communication with non-technical stakeholderst Location - Gurgaon, Pune, Bangalore, Chennai, Bhopal, Jaipur, Hyderabad (Work from office)

Big Data Engineer / Infrastructure Developer Liaising with coworkers and clients to elucidate the requirements for each task Conceptualizing and generating infrastructure that allows big data to be accessed and analyzed Reformulating existing frameworks to optimize their functioning Testing such structures to ensure that they are fit for use Preparing raw data for manipulation by data scientists Detecting and correcting errors in your work Ensuring that your work remains backed up and readily accessible to relevant coworkers Remaining up-to-date with industry standards and technological advancements that will improve the quality of your outputs Experience with: Azure ADLS Apache Parquet Iceberg Kubeflow Airflow

Notice period: Immediate 15days Timings:1:00pm 10:00pm (IST) Work Mode: WFO (Mon-Fri) We are seeking a strategic and innovative Senior Data Scientist to join our high-performing Data Science team. In this role, you will lead the design, development, and deployment of advanced analytics and machine learning solutions that directly impact business outcomes. You will collaborate cross-functionally with product, engineering, and business teams to translate complex data into actionable insights and data products. Key Responsibilities Lead and execute end-to-end data science projects, encompassing problem definition, data exploration, model creation, assessment, and deployment. Develop and deploy predictive models, optimization techniques, and statistical analyses to address tangible business needs. Articulate complex findings through clear and persuasive storytelling for both technical experts and non-technical stakeholders. Spearhead experimentation methodologies, such as A/B testing, to enhance product features and overall business outcomes. Partner with data engineering teams to establish dependable and scalable data infrastructure and production-ready models. Guide and mentor junior data scientists, while also fostering team best practices and contributing to research endeavors. Required Qualifications & Skills: Masters or PhD in Computer Science, Statistics, Mathematics, or a related 5+ years of practical experience in data science, including deploying models to Expertise in Python and SQL; Solid background in ML frameworks such as scikit-learn, TensorFlow, PyTorch, and Competence in data visualization tools like Tableau, Power BI, matplotlib, and Comprehensive knowledge of statistics, machine learning principles, and experimental Experience with cloud platforms (AWS, GCP, or Azure) and Git for version Exposure to MLOps tools and methodologies (e.g., MLflow, Kubeflow, Docker, CI/CD). Familiarity with NLP, time series forecasting, or recommendation systems is a Knowledge of big data technologies (Spark, Hive, Presto) is desirable

Strong knowledge of Python, R, and ML frameworks such as scikit-learn, TensorFlow, PyTorch Experience with cloud ML platforms: SageMaker, Azure ML, Vertex AI LLM Experience such as GPT Hands-on experience with data wrangling, feature engineering, and model optimization Also experienced in developing model wrapers Deep understanding of algorithms including regression, classification, clustering, NLP, and deep learning Familiarity with MLOps tools like MLflow, Kubeflow, or Airflow

Strong knowledge of Python, R, and ML frameworks such as scikit-learn, TensorFlow, PyTorch Experience with cloud ML platforms: SageMaker, Azure ML, Vertex AI LLM Experience such as GPT Hands-on experience with data wrangling, feature engineering, and model optimization Also experienced in developing model wrapers Deep understanding of algorithms including regression, classification, clustering, NLP, and deep learning Familiarity with MLOps tools like MLflow, Kubeflow, or Airflow

Product Engineer (Onsite, Hyderabad) Experience: 5 - 8 Years Exp Salary : INR 30-32 Lacs per annum Preferred Notice Period : Within 30 Days Shift : 9:00AM to 6:00PM IST Opportunity Type: Onsite (Hyderabad) Placement Type: Permanent (*Note: This is a requirement for one of Uplers' Clients) Must have skills required : Python, FastAPI, Django, MLFlow, feast, Kubeflow, Numpy, Pandas, Big Data Good to have skills : Banking, Fintech, Product Engineering background IF (One of Uplers' Clients) is Looking for: Product Engineer (Onsite, Hyderabad) who is passionate about their work, eager to learn and grow, and who is committed to delivering exceptional results. If you are a team player, with a positive attitude and a desire to make a difference, then we want to hear from you. Role Overview Description Product Engineer Location: Narsingi, Hyderabad 5 days of work from the Office Client is a Payment gateway processing company Interview Process: Screening round with InfraCloud, followed by a second round with our Director of Engineering. We share the profile with the client, and they take one/two interviews About the Project We are building a high-performance machine learning engineering platform that powers scalable, data-driven solutions for enterprise environments. Your expertise in Python, performance optimization, and ML tooling will play a key role in shaping intelligent systems for data science and analytics use cases. Experience with MLOps, SaaS products, or big data environments will be a strong plus. Role and Responsibilities Design, build, and optimize components of the ML engineering pipeline for scalability and performance. Work closely with data scientists and platform engineers to enable seamless deployment and monitoring of ML models. Implement robust workflows using modern ML tooling such as Feast, Kubeflow, and MLflow. Collaborate with cross-functional teams to design and scale end-to-end ML services across a cloud-native infrastructure. Leverage frameworks like NumPy, Pandas, and distributed compute environments to manage large-scale data transformations. Continuously improve model deployment pipelines for reliability, monitoring, and automation. Requirements 5+ years of hands-on experience in Python programming with a strong focus on performance tuning and optimization. Solid knowledge of ML engineering principles and deployment best practices. Experience with Feast, Kubeflow, MLflow, or similar tools. Deep understanding of NumPy, Pandas, and data processing workflows. Exposure to big data environments and a good grasp of data science model workflows. Strong analytical and problem-solving skills with attention to detail. Comfortable working in fast-paced, agile environments with frequent cross-functional collaboration. Excellent communication and collaboration skills. Nice to Have Experience deploying ML workloads in public cloud environments (AWS, GCP, or Azure). Familiarity with containerization technologies like Docker and orchestration using Kubernetes. Exposure to CI/CD pipelines, serverless frameworks, and modern cloud-native stacks. Understanding of data protection, governance, or security aspects in ML pipelines. Experience Required: 5+ years How to apply for this opportunity: Easy 3-Step Process: 1. Click On Apply! And Register or log in on our portal 2. Upload updated Resume & Complete the Screening Form 3. Increase your chances to get shortlisted & meet the client for the Interview! About Our Client: We foster business expansion through our innovative products and services, facilitating the seamless adoption of cloud-native technologies by companies. Our expertise lies in the revitalization of applications and infrastructure, harnessing the power of cloud-native solutions for enhanced resilience and scalability. As pioneering Kubernetes partners, we have been dedicated contributors to the open-source cloud-native community, consistently achieving nearly 100% growth over the past few years. We take pride in spearheading local chapters of Serverless & Kubernetes Meetup, actively participating in the development of a vibrant community dedicated to cutting-edge technologies within the Cloud and DevOps domains. About Uplers: Uplers is the #1 hiring platform for SaaS companies, designed to help you hire top product and engineering talent quickly and efficiently. Our end-to-end AI-powered platform combines artificial intelligence with human expertise to connect you with the best engineering talent from India. With over 1M deeply vetted professionals, Uplers streamlines the hiring process, reducing lengthy screening times and ensuring you find the perfect fit. Companies like GitLab, Twilio, TripAdvisor, and AirBnB trust Uplers to scale their tech and digital teams effectively and cost-efficiently. Experience a simpler, faster, and more reliable hiring process with Uplers today.

Job Summary: We are seeking a highly skilled MLOps Engineer to design, deploy, and manage machine learning pipelines in Google Cloud Platform (GCP). In this role, you will be responsible for automating ML workflows, optimizing model deployment, ensuring model reliability, and implementing CI/CD pipelines for ML systems. You will work with Vertex AI, Kubernetes (GKE), BigQuery, and Terraform to build scalable and cost-efficient ML infrastructure. The ideal candidate must have a good understanding of ML algorithms, experience in model monitoring, performance optimization, Looker dashboards and infrastructure as code (IaC), ensuring ML models are production-ready, reliable, and continuously improving. You will be interacting with multiple technical teams, including architects and business stakeholders to develop state of the art machine learning systems that create value for the business. Responsibilities: Managing the deployment and maintenance of machine learning models in production environments and ensuring seamless integration with existing systems. Monitoring model performance using metrics such as accuracy, precision, recall, and F1 score, and addressing issues like performance degradation, drift, or bias. Troubleshoot and resolve problems, maintain documentation, and manage model versions for audit and rollback. Analyzing monitoring data to preemptively identify potential issues and providing regular performance reports to stakeholders. Optimization of the queries and pipelines. Modernization of the applications whenever required Qualifications: Expertise in programming languages like Python, SQL Solid understanding of best MLOps practices and concepts for deploying enterprise level ML systems. Understanding of Machine Learning concepts, models and algorithms including traditional regression, clustering models and neural networks (including deep learning, transformers, etc.) Understanding of model evaluation metrics, model monitoring tools and practices. Experienced with GCP tools like BigQueryML, MLOPS, Vertex AI Pipelines (Kubeflow Pipelines on GCP), Model Versioning & Registry, Cloud Monitoring, Kubernetes, etc. Solid oral and written communication skills and ability to prepare detailed technical documentation of new and existing applications. Strong ownership and collaborative qualities in their domain. Takes initiative to identify and drive opportunities for improvement and process streamlining. Bachelors Degree in a quantitative field of mathematics, computer science, physics, economics, engineering, statistics (operations research, quantitative social science, etc.), international equivalent, or equivalent job experience. Bonus Qualifications: Experience in Azure MLOPS, Familiarity with Cloud Billing. Experience in setting up or supporting NLP, Gen AI, LLM applications with MLOps features. Experience working in an Agile environment, understanding of Lean Agile principles.

Seeking an experienced AI Architect to lead the development of our AI and Machine Learning infrastructure and specialized language models. This role will establish and lead our MLOps practices and drive the creation of scalable, production-ready AI/ML systems. Key Responsibilities Discuss the feasibility of AI/ML use cases along with architectural design with business teams and translate the vision of business leaders into realistic technical implementation Play a key role in defining the AI architecture and selecting appropriate technologies from a pool of open-source and commercial offerings Design and implement robust ML infrastructure and deployment pipelines Establish comprehensive MLOps practices for model training, versioning, and deployment Lead the development of HR-specialized language models (SLMs) Implement model monitoring, observability, and performance optimization frameworks Develop and execute fine-tuning strategies for large language models Create and maintain data quality assessment and validation processes Design model versioning systems and A/B testing frameworks Define technical standards and best practices for AI development Optimize infrastructure for cost, performance, and scalability Required Qualifications 7+ years of experience in ML/AI engineering or related technical roles 3+ years of hands-on experience with MLOps and production ML systems Demonstrated expertise in fine-tuning and adapting foundation models Strong knowledge of model serving infrastructure and orchestration Proficiency with MLOps tools (MLflow, Kubeflow, Weights & Biases, etc.) Experience implementing model versioning and A/B testing frameworks Strong background in data quality methodologies for ML training Proficiency in Python and ML frameworks (PyTorch, TensorFlow, Hugging Face) Experience with cloud-based ML platforms (AWS, Azure, Google Cloud) Proven track record of deploying ML models at scale Preferred Qualifications Experience developing AI applications for enterprise software domains Knowledge of distributed training techniques and infrastructure Experience with retrieval-augmented generation (RAG) systems Familiarity with vector databases (Pinecone, Weaviate, Milvus) Understanding of responsible AI practices and bias mitigation Bachelor's or Master's degree in Computer Science, Machine Learning, or related field

Strong knowledge of Python, R, and ML frameworks such as scikit-learn, TensorFlow, PyTorch. Experience with cloud ML platforms: SageMaker, Azure ML, Vertex AI.LLM Experience such as GPT Hands-on experience with data wrangling, feature engineering, and model optimization. Also experienced in developing model wrapers. Deep understanding of algorithms including regression, classification, clustering, NLP, and deep learning. Familiarity with MLOps tools like MLflow, Kubeflow, or Airflow.

ZS s Beyond Healthcare Analytics (BHCA) Team is shaping one of the key growth vector area for ZS, Beyond Healthcare engagement, comprising of clients from industries like Quick service restaurants, Technology, Food & Beverage, Hospitality, Travel, Insurance, Consumer Products Goods & other such industries across North America, Europe & South East Asia region. BHCA India team currently has presence across New Delhi, Pune and Bengaluru offices and is continuously expanding further at a great pace. BHCA India team works with colleagues across clients and geographies to create and deliver real world pragmatic solutions leveraging AI SaaS products & platforms, Generative AI applications, and other Advanced analytics solutions at scale. What You ll Do: Build, Refine and Use ML Engineering platforms and components. Scaling machine learning algorithms to work on massive data sets and strict SLAs. Build and orchestrate model pipelines including feature engineering, inferencing and continuous model training. Implement ML Ops including model KPI measurements, tracking, model drift & model feedback loop. Collaborate with client facing teams to understand business context at a high level and contribute in technical requirement gathering. Implement basic features aligning with technical requirements. Write production-ready code that is easily testable, understood by other developers and accounts for edge cases and errors. Ensure highest quality of deliverables by following architecture/design guidelines, coding best practices, periodic design/code reviews. Write unit tests as well as higher level tests to handle expected edge cases and errors gracefully, as well as happy paths. Uses bug tracking, code review, version control and other tools to organize and deliver work. Participate in scrum calls and agile ceremonies, and effectively communicate work progress, issues and dependencies. Consistently contribute in researching & evaluating latest architecture patterns/technologies through rapid learning, conducting proof-of-concepts and creating prototype solutions. What You ll Bring A master's or bachelor s degree in Computer Science or related field from a top university. 4+ years hands-on experience in ML development. Good understanding of the fundamentals of machine learning Strong programming expertise in Python, PySpark/Scala. Expertise in crafting ML Models for high performance and scalability. Experience in implementing feature engineering, inferencing pipelines, and real time model predictions. Experience in ML Ops to measure and track model performance, experience working with MLFlow Experience with Spark or other distributed computing frameworks. Experience in ML platforms like Sage maker, Kubeflow. Experience with pipeline orchestration tools such Airflow. Experience in deploying models to cloud services like AWS, Azure, GCP, Azure ML. Expertise in SQL, SQL DB's. Knowledgeable of core CS concepts such as common data structures and algorithms. Collaborate well with teams with different backgrounds / expertise / functions

Gen AI + DS + ML Ops Job Title: Generative AI and Data Science Engineer with MLOps Expertise Location: Gurgaon, India Employment Type: Full-time About the Role: We are seeking a versatile and highly skilled Generative AI and Data Science Engineer with strong MLOps expertise. This role combines deep technical knowledge in data science and machine learning with a focus on designing and deploying scalable, production-level AI solutions. You will work with cross-functional teams to drive AI/ML projects from research and prototyping through to deployment and maintenance, ensuring model robustness, scalability, and efficiency. Responsibilities: Generative AI Development and Data Science: Design, develop, and fine-tune generative AI models for various applications such as natural language processing, image synthesis, and data augmentation. Perform exploratory data analysis (EDA) and statistical modeling to identify trends, patterns, and actionable insights. Collaborate with data engineering and product teams to create data pipelines for model training, testing, and deployment. Apply data science techniques to optimize model performance and address real-world business challenges. Machine Learning Operations (MLOps): Implement MLOps best practices for managing and automating the end-to-end machine learning lifecycle, including model versioning, monitoring, and retraining. Build, maintain, and optimize CI/CD pipelines for ML models to streamline development and deployment processes. Ensure scalability, robustness, and security of AI/ML systems in production environments. Develop tools and frameworks for monitoring model performance and detecting anomalies post-deployment. Research and Innovation: Stay current with advancements in generative AI, machine learning, and MLOps technologies and frameworks. Identify new methodologies, tools, and technologies that could enhance our AI and data science capabilities. Engage in R&D initiatives and collaborate with team members on innovative projects. Requirements: Educational Background: Bachelors or Masters degree in Computer Science, Data Science, Engineering, or a related field. PhD is a plus. Technical Skills: Proficiency in Python and familiarity with machine learning libraries (e.g., TensorFlow, PyTorch, Keras, scikit-learn). Strong understanding of generative AI models (e.g., GANs, VAEs, transformers) and deep learning techniques. Experience with MLOps frameworks and tools such as MLflow, Kubeflow, Docker, and CI/CD platforms. Knowledge of data science techniques for EDA, feature engineering, statistical modeling, and model evaluation. Familiarity with cloud platforms (e.g., AWS, Google Cloud, Azure) for deploying and scaling AI/ML models. Soft Skills: Ability to collaborate effectively across teams and communicate complex technical concepts to non-technical stakeholders. Strong problem-solving skills and the ability to innovate in a fast-paced environment. Preferred Qualifications: Prior experience in designing and deploying large-scale generative AI models. Proficiency in SQL and data visualization tools (e.g., Tableau, Power BI). Experience with model interpretability and explainability frameworks.

Job Summary: We are looking for a highly capable and automation-driven MLOps Engineer with 2+ years of experience in building and managing end-to-end ML infrastructure. This role focuses on operationalizing ML pipelines using tools like DVC, MLflow, Kubeflow, and Airflow, while ensuring efficient deployment, versioning, and monitoring of machine learning and Generative AI models across GPU-based cloud infrastructure (AWS/GCP). The ideal candidate will also have experience in multi-modal orchestration, model drift detection, and CI/CD for ML systems. Key Responsibilities: Develop, automate, and maintain scalable ML pipelines using tools such as Kubeflow, MLflow, Airflow, and DVC. Set up and manage CI/CD pipelines tailored to ML workflows, ensuring reliable model training, testing, and deployment. Containerize ML services using Docker and orchestrate them using Kubernetes in both development and production environments. Manage GPU infrastructure and cloud-based deployments (AWS, GCP) for high-performance training and inference. Integrate Hugging Face models and multi-modal AI systems into robust deployment frameworks. Monitor deployed models for drift, performance degradation, and inference bottlenecks, enabling continuous feedback and retraining. Ensure proper model versioning, lineage, and reproducibility for audit and compliance. Collaborate with data scientists, ML engineers, and DevOps teams to build reliable and efficient MLOps systems. Support Generative AI model deployment with scalable architecture and automation-first practices. Qualifications: 2+ years of experience in MLOps, DevOps for ML, or Machine Learning Engineering. Hands-on experience with MLflow, DVC, Kubeflow, Airflow, and CI/CD tools for ML. Proficiency in containerization and orchestration using Docker and Kubernetes. Experience with GPU infrastructure, including setup, scaling, and cost optimization on AWS or GCP. Familiarity with model monitoring, drift detection, and production-grade deployment pipelines. Good understanding of model lifecycle management, reproducibility, and compliance. Preferred Qualifications : Experience deploying Generative AI or multi-modal models in production. Knowledge of Hugging Face Transformers, model quantization, and resource-efficient inference. Familiarity with MLOps frameworks and observability stacks. Experience with security, governance, and compliance in ML environments. Location-Delhi NCR,Bangalore,Chennai,Pune,Kolkata,Ahmedabad,Mumbai,Hyderabad

We are seeking a highly experienced and technically adept Lead AI/ML Engineer to spearhead the development and deployment of cutting-edge AI solutions, with a focus on Generative AI and Natural Language Processing (NLP). The ideal candidate will be responsible for leading a high-performing team, architecting scalable ML systems, and driving innovation across AI/ML projects using modern toolchains and cloud-native technologies. Key Responsibilities Team Leadership: Lead, mentor, and manage a team of data scientists and ML engineers; drive technical excellence and foster a culture of innovation. AI/ML Solution Development: Design and deploy end-to-end machine learning and AI solutions, including Generative AI and NLP applications. Conversational AI: Build LLM-based chatbots and document intelligence tools using frameworks like LangChain , Azure OpenAI , and Hugging Face . MLOps Execution: Implement and manage the full ML lifecycle using tools such as MLFlow , DVC , and Kubeflow to ensure reproducibility, scalability, and efficient CI/CD of ML models. Cross-functional Collaboration: Partner with business and engineering stakeholders to translate requirements into impactful AI solutions. Visualization & Insights: Develop interactive dashboards and data visualizations using Streamlit , Tableau , or Power BI for presenting model results and insights. Project Management: Own delivery of projects with clear milestones, timelines, and communication of progress and risks to stakeholders. Required Skills & Qualifications Languages & Frameworks: Proficient in Python and frameworks like TensorFlow , PyTorch , Keras , FastAPI , Django NLP & Generative AI: Hands-on experience with BERT , LLaMA , Spacy , LangChain , Hugging Face , and other LLM-based technologies MLOps Tools: Experience with MLFlow , Kubeflow , DVC , ClearML for managing ML pipelines and experiment tracking Visualization: Strong in building visualizations and apps using Power BI , Tableau , Streamlit Cloud & DevOps: Expertise with Azure ML , Azure OpenAI , Docker , Jenkins , GitHub Actions Databases & Data Engineering: Proficient with SQL/NoSQL databases and handling large-scale datasets efficiently Preferred Qualifications Masters or PhD in Computer Science, AI/ML, Data Science, or related field Experience working in agile product development environments Strong communication and presentation skills with technical and non-technical stakeholders

As a AI/ML Engineer, you will be responsible for designing, developing, and implementing machine learning algorithms and AI solutions that address complex business challenges. You will lead a team of engineers collaborating with cross-functional teams to drive the successful deployment of AI initiatives. Your expertise will be crucial in shaping our AI strategy and ensuring the delivery of high-quality, scalable solutions. Key Responsibilities: Lead the design and development of machine learning models and AI solutions to solve business problems. Select appropriate machine learning or deep learning models based on problem context and data availability. Develop, train, test, and validate models using state-of-the-art methodologies and frameworks. Collaborate with analytics, developers and domain experts to acquire, clean, and preprocess large datasets. Engineer features and performs exploratory data analysis to ensure data quality and model readiness. Containerize models (Docker/Kubernetes), implement monitoring (Prometheus/Grafana), and automate pipelines (MLflow/Kubeflow). Implement models into production environments, ensuring robustness, scalability, and maintainability. Develop and maintain CI/CD pipelines for seamless model integration and deployment. Monitor and evaluate model performance post-deployment and iterate based on feedback and performance metrics. Document model architecture, development processes, and performance evaluations thoroughly. Share insights and technical know-how with team members to foster a culture of continuous learning and improvement. Research & Innovation: Stay ahead of AI trends (LLMs, generative AI) and advocate for ethical AI practices. Analyze large datasets to extract insights and improve model performance. Ensure compliance with data privacy and security regulations in all AI/ML initiatives. Qualifications: Bachelor's or master's degree in computer science, Data Science, Machine Learning, or a related field. Proven experience (3+ years) in AI/ML engineering, with a strong portfolio of successful projects. Proficiency in programming languages such as Python, R, or Java, and experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). Strong understanding of machine learning algorithms, statistical modeling, and data analysis techniques. Experience with cloud platforms (e.g., AWS, Azure, Google Cloud)

Your Day-to-Day Provide technical leadership and guidance to teams of software engineers, fostering a culture of collaboration, innovation, and continuous improvement. Establish outcomes and key results (OKRs) and successfully deliver them. Drive improvements in key performance indicators (KPIs). Increase the productivity and velocity of delivery teams. Develop, plan, and execute engineering roadmaps that bring value and quality to our customers. Collaborate and coordinate across teams and functions to ensure technical, product, and business objectives are met. Instill end-to-end ownership of products, projects, features, modules, and services that you and your team deliver in all phases of the software development lifecycle. What do you need to bring 10+ years of experience in the software industry, with 3+ years of professional experience leading software development teams. Strong critical thinking and problem-solving skills with the ability to address complex technical and non-technical challenges. Experience building and developing engineering teams that exhibit strong ownership, user empathy, and engineering excellence. Proven track record of delivering high-quality systems and software in Big Data Technologies including Spark, Airflow, Hive, etc., with practical exposure to integrating machine learning workflows into data pipelines. Proven track record of delivering high-quality systems and software in Java/J2EE technologies and distributed systems, with experience deploying ML models into production at scale using REST APIs, streaming platforms, or batch inference. Excellent communication skills with the ability to collaborate effectively with cross-functional teams (including data scientists and ML engineers) and manage stakeholders expectations. Ability to coach and mentor talent to reach their full potential, including guiding teams in adopting MLOps best practices and understanding AI model lifecycle management. Experience in building large scale, high throughput, low latency systems, including real-time data processing systems that support personalization, anomaly detection, or predictive analytics. Strong understanding of software development methodologies, modern technology topics and frameworks, and developer operations best practices. Experience with ML platforms (e.g., Kubeflow, MLflow) and familiarity with model monitoring, feature engineering, and data versioning tools is a plus. Provide leadership to others, particularly junior engineers who work on the same team or related product features. Proven experience delivering complex software projects and solutions effectively through Agile methodologies on a regular release cadence. Strong verbal and written communication skills. Strong customer focus, ownership, urgency and drive.

AI/ML Architect Experience 10+ years in total, 8+ years in AI/ML development 3+ years in AI/ML architecture Education Bachelors/Masters in CS, AI/ML, Engineering, or similar Title: AI/ML Architect Location: Onsite Bangalore Experience: 10+ years Position Summary: We are seeking an experienced AI/ML Architect to lead the design and deployment of scalable AI solutions. This role requires a strong blend of technical depth, systems thinking, and leadership in machine learning , computer vision , and real-time analytics . You will drive the architecture for edge, on-prem, and cloud-based AI systems, integrating 3rd party data sources, sensor and vision data to enable predictive, prescriptive, and autonomous operations across industrial environments. Key Responsibilities: Architecture & Strategy Define the end-to-end architecture for AI/ML systems including time series forecasting , computer vision , and real-time classification . Design scalable ML pipelines (training, validation, deployment, retraining) using MLOps best practices. Architect hybrid deployment models supporting both cloud and edge inference for low-latency processing. Model Integration Guide the integration of ML models into the IIoT platform for real-time insights, alerting, and decision support. Support model fusion strategies combining disparate data sources, sensor streams with visual data (e.g., object detection + telemetry + 3rd party data ingestion). MLOps & Engineering Define and implement ML lifecycle tooling, including version control, CI/CD, experiment tracking, and drift detection. Ensure compliance, security, and auditability of deployed ML models. Collaboration & Leadership Collaborate with Data Scientists, ML Engineers, DevOps, Platform, and Product teams to align AI efforts with business goals. Mentor engineering and data teams in AI system design, optimization, and deployment strategies. Stay ahead of AI research and industrial best practices; evaluate and recommend emerging technologies (e.g., LLMs, vision transformers, foundation models). Must-Have Qualifications: Bachelors or Master’s degree in Computer Science, AI/ML, Engineering, or a related technical field. 8+ years of experience in AI/ML development, with 3+ years in architecting AI solutions at scale. Deep understanding of ML frameworks (TensorFlow, PyTorch), time series modeling, and computer vision. Proven experience with object detection, facial recognition, intrusion detection , and anomaly detection in video or sensor environments. Experience in MLOps (MLflow, TFX, Kubeflow, SageMaker, etc.) and model deployment on Kubernetes/Docker . Proficiency in edge AI (Jetson, Coral TPU, OpenVINO) and cloud platforms (AWS, Azure, GCP). Nice-to-Have Skills: Knowledge of stream processing (Kafka, Spark Streaming, Flink). Familiarity with OT systems and IIoT protocols (MQTT, OPC-UA). Understanding of regulatory and safety compliance in AI/vision for industrial settings. Experience with charts, dashboards, and integrating AI with front-end systems (e.g., alerts, maps, command center UIs). Role Impact: As AI/ML Architect, you will shape the intelligence layer of our IIoT platform — enabling smarter, safer, and more efficient industrial operations through AI. You will bridge research and real-world impact , ensuring our AI stack is scalable, explainable, and production-grade from day one.

5+ years of experience in Python programing and performance tuning and optimization. Experience on ML engineering, Knowledge on Feast, Kubeflow and MLFlow. Deep understanding on NumPy , Pandas, data frames etc. Working knowledge on bigdata environment and data science model added advantage. Strong analytical and problem-solving skills, with attention to detail and ability to work in a fast-paced environment Excellent communication and collaboration skills, with ability to work with cross-functional teams Knowledge of machine learning and data science concepts

Role Summary: The AI/ML Platform Engineering Lead is a pivotal leadership role responsible for managing the day-to-day operations and development of the AI/ML platform team. In this role, you will guide the team in designing, building, and maintaining scalable platforms, while collaborating with other engineering and data science teams to ensure successful model deployment and lifecycle management. You can apply directly using the link below: https://jobs.lever.co/welocalize/628f87a7-4edd-4b4f-b440-3b602aa4dadc Key Responsibilities: Lead and manage a team of platform engineers in developing and maintaining robust AI/ML platforms. Define and implement best practices for machine learning infrastructure, ensuring scalability, performance, and security. Collaborate closely with data scientists and DevOps teams to optimize the ML lifecycle from model training to deployment. Establish and enforce standards for platform automation, monitoring, and operational efficiency. Serve as the primary liaison between engineering teams, product teams, and leadership. Mentor and develop junior engineers, providing technical guidance and performance feedback. Stay abreast of the latest advancements in AI/ML infrastructure and integrate new technologies where applicable. Qualifications: Bachelors or Masters degree in Computer Science, Engineering, or a related field. 8+ years of experience in AI/ML platform development and infrastructure. Proven experience in leading engineering teams and driving large-scale projects. Extensive expertise in cloud infrastructure (AWS, GCP, Azure), MLOps tools (e.g., Kubeflow, MLflow), and infrastructure as code (Terraform) Strong programming skills in Python and Node.js , with a proven track record of building scalable and maintainable systems that support AI/ML workflows. Hands-on experience with monitoring and observability tools, such as Datadog, to ensure platform reliability and performance. Strong leadership and communication skills with the ability to influence cross-functional teams. Excellent problem-solving skills and the ability to work in a fast-paced, collaborative environment.