14 Model Monitoring Jobs

About the Team and Our Scope We are a forward-thinking tech organization within Swiss Re, delivering transformative AI/ML solutions that redefine how businesses operate. Our mission is to build intelligent, secure, and scalable systems that deliver real-time insights, automation, and high-impact user experiences to clients globally. You'll join a high-velocity AI/ML team working closely with product managers, architects, and engineers to create next-gen enterprise-grade solutions. Our team is built on a startup mindset - bias to action, fast iterations, and ruthless focus on value delivery. We're not only shaping the future of AI in business - we're shaping the future of talent. This role is ideal for someone passionate about advanced AI engineering today and curious about evolving into a product leadership role tomorrow. You'll get exposure to customer discovery, roadmap planning, and strategic decision-making alongside your technical contributions. Role Overview As an AI/ML Engineer, you will play a pivotal role in the research, development, and deployment of next-generation GenAI and machine learning solutions . Your scope will go beyond retrieval-augmented generation (RAG) to include areas such as prompt engineering, long-context LLM orchestration, multi-modal model integration (voice, text, image, PDF), and agent-based workflows. You will help assess trade-offs between RAG and context-native strategies, explore hybrid techniques, and build intelligent pipelines that blend structured and unstructured data. You'll work with technologies such as LLMs, vector databases, orchestration frameworks, prompt chaining libraries, and embedding models, embedding intelligence into complex, business-critical systems. This role sits at the intersection of rapid GenAI prototyping and rigorous enterprise deployment, giving you hands-on influence over both the technical stack and the emerging product direction. Key Responsibilities Build Next-Gen GenAI Pipelines : Design, implement, and optimize pipelines across RAG, prompt engineering, long-context input handling, and multi-modal processing. Prototype, Validate, Deploy : Rapidly test ideas through PoCs, validate performance against real-world business use cases, and industrialize successful patterns. Ingest, Enrich, Embed: Construct ingestion workflows including OCR, chunking, embeddings, and indexing into vector databases to unlock unstructured data. Integrate Seamlessly: Embed GenAI services into critical business workflows, balancing scalability, compliance, latency, and observability. Explore Hybrid Strategies: Combine RAG with context-native models, retrieval mechanisms, and agentic reasoning to build robust hybrid architectures. Drive Impact with Product Thinking : Collaborate with product managers and UX designers to shape user-centric solutions and understand business context. Ensure Enterprise-Grade Quality: Deliver solutions that are secure, compliant (e.g., GDPR), explainable, and resilient - especially in regulated environments. What Makes You a Fit Must-Have Technical Expertise Proven experience with GenAI techniques and LLMs , including RAG, long-context inference, prompt tuning, and multi-modal integration. Strong hands-on skills with Python , embedding models, and orchestration libraries (e.g., LangChain, Semantic Kernel, or equivalents). Comfort with MLOps practices , including version control, CI/CD pipelines, model monitoring, and reproducibility. Ability to operate independently, deliver iteratively, and challenge assumptions with data-driven insight. Understanding of vector search optimization and retrieval tuning. Exposure to multi-modal models Nice-To-Have Qualifications Experience building and operating AI systems in regulated industries (e.g., insurance, finance, healthcare). Familiarity with Azure AI ecosystem (e.g., Azure OpenAI, Azure AI Document Intelligence, Azure Cognitive Search) and deployment practices in cloud-native environments. Experience with agentic AI architectures , tools like AutoGen, or prompt chaining frameworks. Familiarity with data privacy and auditability principles in enterprise AI. Bonus: You Think Like a Product Manager While this role is technical at its core, we highly value candidates who are curious about how AI features become products . If you're excited by the idea of influencing roadmaps, shaping requirements, or owning end-to-end value delivery - we'll give you space to grow into it. This is a role where engineering and product are not silos . If you're keen to move in that direction, we'll mentor and support your evolution. Why Join Us You'll be part of a team that's pushing AI/ML into uncharted, high-value territory. We operate with urgency, autonomy, and deep collaboration. You'll prototype fast, deliver often, and see your work shape real-world outcomes - whether in underwriting, claims, or data orchestration. And if you're looking to transition from deep tech to product leadership , this role is a launchpad. Swiss Re is an equal opportunity employer . We celebrate diversity and are committed to creating an inclusive environment for all employees. Keywords: Reference Code: 134317

- Hands-on experience with Jupyter Notebooks, Google Colab, Git & GitHub .- Solid understanding of Data Visualization Tools and Dashboard Creation. - Prior teaching/training experience (online/offline) is a plus .- Excellent communication and presentati

Design, develop, implement AI and Generative AI solutions to address business problems and achieveobjectives. Gather, clean, and prepare large datasets to ensure readiness for AI model training Train, fine-tune, evaluate, andoptimizeAI models for specific use cases, ensuring accuracy, performance, cost-effectiveness, and scalability. Seamlessly integrate AI models and autonomous agent solutions into cloud-based & on-prem products to drive smarter workflows and improved productivity. Develop reusable tools, libraries, and components that standardize and accelerate the development of AI solutions across the organization. Monitor andmaintaindeployed models, ensuring consistent performance and reliability in production environments Stay up to date with the latest AI/ML advancements, exploringnew technologies, algorithms, and methodologies to enhance product capabilities. Effectively communicate technical concepts, research findings, and AI solution strategies to both technical and non-technical stakeholders. Understand the IBM tool and model landscape and work closely with cross-functional teams toleveragethese tools, driving innovation and alignment. Lead and mentor team members to improve performance. Collaborate with operations, architects, and product teams to resolve issues and define product designs. Exercise best practices in agile development and software engineering.Code, unit test, debug and perform integration tests of software components Participatein software design reviews, code reviews and project planning. Write and review documentation and technical blog posts. Contribute to department attainment of organizationalobjectivesand high customer satisfaction Required education Bachelor's Degree Preferred education Bachelor's Degree Required technical and professional expertise Minimum 6 years of hands-on experience developing AI-based applications using Python. 2+ years in Performance testing, Reliability testing 2+ years of experience using deep learning frameworks (TensorFlow,PyTorch, orKeras) Solid understanding of ML/AI conceptsEDA, preprocessing, algorithm selection, machine learning frameworks, model efficiency metrics, model monitoring. Familiarity with Natural Language Processing (NLP) techniques. Deep understanding of Large Language Models (LLM) Architectures, theircapabilitiesand limitations. Provenexpertisein integrating and working with LLMs to build robust AI solutions. Skilled in crafting effective prompts to guide LLMs to provide desired outputs. Hands-on experience with LLM frameworks such asLangchain,Langraph,CrewAIetc., Experience in LLM application development based on Retrieval-Augmented Generation (RAG) concept, familiarity with vector databases, and fine-tuning large language models (LLMs) to enhance performance and accuracy. Proficient in microservices development using Python (Django/Flask or similar technologies). Experience in Agile development methodologies Familiarity with platforms like Kubernetes and experience building on top of the native platforms Experience with cloud-based data platforms and services (e.g., IBM, AWS, Azure, Google Cloud). Experience designing, building, andmaintainingdata processing systems working in containerized environments (Docker, OpenShift, k8s) Excellent communication skills with the ability to effectively collaborate with technical and non-technical stakeholders Preferred technical and professional experience Experience in MLOPs frameworks (BentoML,Kubefloworsimilar technologies) and exposure to LLMOPs Experience in cost optimisation initiatives Experience with end-to-end chatbot development, including design, deployment, and ongoing optimization,leveragingNLPand integrating with backend systems and APIs. Understanding of security and ethical best practices for data and model development Contributions toopen sourceprojects

Role & responsibilities : Job Title : AI Engineer (AI-Powered Agents, Knowledge Graphs, & MLOps) Location: Hyderabad Job Type : Full-time Hands-on Gen AI development in GCP and Azure stack Job Summary : We seek an AI Engineer with deep expertise in building AI-powered agents, designing and implementing knowledge graphs, and optimizing business processes through AI-driven solutions. The role also requires hands-on experience in AI Operations (AI Ops), including continuous integration/deployment (CI/CD), model monitoring, and retraining. The ideal candidate will have experience working with open-source or commercial large language models (LLMs) and be proficient in using platforms like Azure Machine Learning Studio or Google Vertex AI to scale AI solutions effectively. Key Responsibilities : AI Agent Development : Design, build, and deploy AI-powered agents for applications such as virtual assistants, customer service bots, and task automation systems using LLMs and other AI models. Knowledge Graph Implementation : Develop and implement knowledge graphs for enterprise data integration, enhancing the retrieval, structuring, and management of large datasets to support decision-making. AI-Driven Process Optimization : Collaborate with business units to optimize workflows using AI-driven solutions, automating decision-making processes and improving operational efficiency. AI Ops (MLOps) : Implement robust AI/ML pipelines that follow CI/CD best practices to ensure continuous integration and deployment of AI models across different environments. Model Monitoring and Maintenance : Establish processes for real-time model monitoring, including tracking performance, drift detection, and accuracy of models in production environments. Model Retraining and Optimization : Develop automated or semi-automated pipelines for model retraining based on changes in data patterns or model performance. Implement processes to ensure continuous improvement and accuracy of AI solutions. Cloud and ML Platforms : Utilize platforms such as Azure Machine Learning Studio, Google Vertex AI, and open-source frameworks for end-to-end model development, deployment, and monitoring. Collaboration : Work closely with data scientists, software engineers, and business stakeholders to deploy scalable AI solutions that deliver business impact. MLOps Tools : Leverage MLOps tools for version control, model deployment, monitoring, and automated retraining processes to ensure operational stability and scalability of AI systems. Performance Optimization : Continuously optimize models for scalability and performance, identifying bottlenecks and improving efficiencies. Qualifications : Bachelors or Masters degree in Computer Science, Artificial Intelligence, Data Science, or a related field. 3+ years of experience as an AI Engineer, focusing on AI-powered agent development, knowledge graphs, AI-driven process optimization, and MLOps practices. Proficiency in working with large language models (LLMs) such as GPT-3/4, GPT-J, BLOOM, or similar, including both open-source and commercial variants. Experience with knowledge graph technologies, including ontology design and graph databases (e.g., Neo4j, AWS Neptune). AI Ops/MLOps Expertise : Hands-on experience with AI/ML CI/CD pipelines, automated model deployment, and continuous model monitoring in production environments. Familiarity with tools and frameworks for model lifecycle management, such as MLflow, Kubeflow, or similar. Strong skills in Python, Java, or similar languages, and proficiency in building, deploying, and monitoring AI models. Solid experience in natural language processing (NLP) techniques, including building conversational AI, entity recognition, and text generation models. Model Monitoring & Retraining : Expertise in setting up automated pipelines for model retraining, monitoring for drift, and ensuring the continuous performance of deployed models. Experience in using cloud platforms like Azure Machine Learning Studio, Google Vertex AI, or similar cloud-based AI/ML tools. Preferred Skills : Experience with building or integrating conversational AI agents using platforms like Microsoft Bot Framework, Rasa, or Dialogflow. Familiarity with AI-driven business process automation and RPA integration using AI/ML models. Knowledge of advanced AI-driven process optimization tools and techniques, including AI orchestration for enterprise workflows. Experience with containerization technologies (e.g., Docker, Kubernetes) to support scalable AI/ML model deployment. Certification in Azure AI Engineer Associate, Google Professional Machine Learning Engineer, or relevant MLOps-related certifications is a plus. Preferred candidate profile Perks and benefits

AI Ops Senior Architect 12 -17 Years Work Location - Pune/ Bengaluru/Hyderabad/Chennai/ Gurugram Tredence is Data science, engineering, and analytics consulting company that partners with some of the leading global Retail, CPG, Industrial and Telecom companies. We deliver business impact by enabling last mile adoption of insights by uniting our strengths in business analytics, data science and data engineering. Headquartered in the San Francisco Bay Area, we partner with clients in US, Canada, and Europe. Bangalore is our largest Centre of Excellence with skilled analytics and technology teams serving our growing base of Fortune 500 clients. JOB DESCRIPTION At Tredence, you will lead the evolution of Industrializing AI ” solutions for our clients by implementing ML/LLM/GenAI & Agent Ops best practices. You will lead the Architecture , Design & development of large scale ML/LLMOps platforms for our clients. You’ll build and maintain tools for deployment, monitoring, and operations. You’ll be a trusted advisor to our clients in ML/GenAI/Agent Ops space & coach to the ML engineering practitioners to build effective solutions to Industrialize AI solutions THE IDEAL CANDIDATE WILL BE RESPONSIBLE FOR AI Ops Strategy, Innovation, Research and Technical Standards 1. Conduct research and experiment with emerging AI Ops technologies and trends. Create POV’s, POC’s & present Proof of Technology to use latest tools, Technologies & services from Hyper scalers focussed on ML, GenAI & Agent Ops 2. Define and propose new technical standards and best practices for the organization's AI Ops environment. 3. Lead the evaluation and adoption of innovative MLOps solutions to address critical business challenges. 4. Conduct meet ups, attend & present in Industry events, conferences, etc 5. Ideate & develop accelerators to strengthen service offerings of AI Ops practice Solution Design & Architectural Development 6. Lead Design & architecture of scalable model training & deployment pipelines for large-scale deployments 7. Architect & Design large scale ML & GenAI Ops platforms 8. Collaborate with Data science & GenAI practice to define and implement strategies of AI solutions for model explainability and interpretability 9. Mentor and guide senior architects in crafting cutting-edge AI Ops solutions 10. Lead architecture reviews and identify opportunities for significant optimizations and improvements. Documentation and Best Practices 11. Develop and maintain comprehensive documentation of AIOps architectures designs and best practices. 12. Lead the development and delivery of training materials and workshops on AIOps tools and techniques. 13. Actively participate in sharing knowledge and expertise with the MLOps team through internal presentations and code reviews. Qualifications and Skills: 1. Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field with minimum 12 years of experience 2. Proven experience in architecting & developing AIOps solutions – to streamline Machine Learning & GenAI development lifecycle 3. Proven experience as an AI Ops Architect – ML & GenAI in architecting & design of ML & GenAI platforms 4. Hands on experience in Model deployment strategies, Designing ML & GenAI model pipelines to scale in production, Model Observability techniques used to monitor performance of ML & LLM’s 5. Strong coding skills with experience in implementing best coding practices Technical Skills & Expertise Python, PySpark, PyTorch ,Java, Micro Services, API’s LLMOps – Vector DB, RAG, LLM Orchestration tools, LLM Observability, LLM Guardrails, Responsible AI MLOps - MLFlow, ML/DL libraries, Model & Data Drift Detection libraries & techniques Real Time & Batch Streaming Container Orchestration Platforms Cloud platforms – Azure/ AWS/ GCP, Data Platforms – Databricks/ Snowflake Nice to Have: Understanding of Agent Ops Exposure to Databricks platform You can expect to – Work with world’s biggest Retailers, CPG’s, HealthCare, Banking & Manufacturing customers and help them solve some of their most critical problems Create multi-million Dollar business opportunities by leveraging impact mindset, cutting edge solutions and industry best practices. Work in a diverse environment that keeps evolving Hone your entrepreneurial skills as you contribute to growth of the organization

Job Description Designation: AI Engineer Experience range: 2-5 years relevant experience Location: Pune Key Responsibilities: Develop and deploy scalable AI-powered applications using the Python stack. Leverage cutting-edge AI/ML technologies such as LangChain, LangGraph, AutoGen, Phidata, CrewAI Hugging Face, OpenAI APIs, PyTorch, TensorFlow, and other advanced frameworks to build innovative AI applications. Write clean, efficient, and well-documented code adhering to best practic Build and manage robust APIs for integrating AI solutions into applications. Research and experiment with emerging technologies to discover new AI-driven use cases. Deploy and manage AI solutions in cloud environments (AWS, Azure, GCP), ensuring security, scalability, and performance. Collaborate with product managers, engineers, and UX/UI designers to define AI application requirements and ali them with business objectives. Apply MLOps principles to streamline AI model deployment, monitoring, and optimization. Solve complex problems using foundational knowledge of generative AI, machine learning, and data processing techniques. Contribute to continuous improvement of development processes and practices. Resolve production issues by conducting effective troubleshooting and root cause analysis (RCA) within SLA Work with operations teams to support product deployment and issue resolution. Requirements Educational Background: Bachelors or Masters degree in Computer Science or related fields with a strong academic track record. Must have graduated from NIT, IIIT, IIT, or BITS Pilani colleges only. Experience Technical Skills: 3+ years of hands-on experience in building and deploying AI applications on Python stack. Strong knowledge of Python and related frameworks. Good knowledge of few of the AI/ML frameworks and agentic frameworks and platforms like LangChain, LangGraph, AutoGen, Hugging Face, CrewAI, OpenAI APIs, PyTorch, and TensorFlow etc. Experience with AI/ML workflows, including data preparation, model deployment, and optimizations. Proficiency in building and consuming RESTful APIs for connecting AI models with web applications. Knowledge of MLOps tools and practices, including model lifecycle management, CI/CD for AI, and model monitoring. Familiarity with cloud platforms like AWS, Azure, or Google Cloud, including containerization (Docker) and orchestration (Kubernetes). Experience with CI/CD pipelines, version control (Git), and automation frameworks. Strong understanding of algorithms, AI/ML fundamentals, and data preprocessing techniques. Soft Skills: Passion for exploring, experimenting and implementing emerging AI technologies. Self-starter who can independently and collaboratively in a fast-paced environment. Excellent problem-solving and analytical abilities to tackle complex challenges. Effective communication skills to explain AI concepts and strategies to stakeholders Why Join Us? Be part of a forward-thinking company revolutionizing air and port cargo logistics with AI. Collaborate with a team passionate about innovation and excellence. Gain exposure to cutting-edge AI technologies and frameworks. Enjoy opportunities for professional growth and upskilling in the latest tech stack. Receive a competitive compensation and benefits package.

Key Responsibilities AI Model Deployment & Integration: Deploy and manage AI/ML models, including traditional machine learning and GenAI solutions (e.g., LLMs, RAG systems). Implement automated CI/CD pipelines for seamless deployment and scaling of AI models. Ensure efficient model integration into existing enterprise applications and workflows in collaboration with AI Engineers. Optimize AI infrastructure for performance and cost efficiency in cloud environments (AWS, Azure, GCP). Monitoring & Performance Management: Develop and implement monitoring solutions to track model performance, latency, drift, and cost metrics. Set up alerts and automated workflows to manage performance degradation and retraining triggers. Ensure responsible AI by monitoring for issues such as bias, hallucinations, and security vulnerabilities in GenAI outputs. Collaborate with Data Scientists to establish feedback loops for continuous model improvement. Automation & MLOps Best Practices: Establish scalable MLOps practices to support the continuous deployment and maintenance of AI models. Automate model retraining, versioning, and rollback strategies to ensure reliability and compliance. Utilize infrastructure-as-code (Terraform, CloudFormation) to manage AI pipelines. Security & Compliance: Implement security measures to prevent prompt injections, data leakage, and unauthorized model access. Work closely with compliance teams to ensure AI solutions adhere to privacy and regulatory standards (HIPAA, GDPR). Regularly audit AI pipelines for ethical AI practices and data governance. Collaboration & Process Improvement: Work closely with AI Engineers, Product Managers, and IT teams to align AI operational processes with business needs. Contribute to the development of AI Ops documentation, playbooks, and best practices. Continuously evaluate emerging GenAI operational tools and processes to drive innovation. Qualifications & Skills Education: Bachelors or Masters degree in Computer Science, Data Engineering, AI, or a related field. Relevant certifications in cloud platforms (AWS, Azure, GCP) or MLOps frameworks are a plus. Experience: 3+ years of experience in AI/ML operations, MLOps, or DevOps for AI-driven solutions. Hands-on experience deploying and managing AI models, including LLMs and GenAI solutions, in production environments. Experience working with cloud AI platforms such as Azure AI, AWS SageMaker, or Google Vertex AI. Technical Skills: Proficiency in MLOps tools and frameworks such as MLflow, Kubeflow, or Airflow. Hands-on experience with monitoring tools (Prometheus, Grafana, ELK Stack) for AI performance tracking. Experience with containerization and orchestration tools (Docker, Kubernetes) to support AI workloads. Familiarity with automation scripting using Python, Bash, or PowerShell. Understanding of GenAI-specific operational challenges such as response monitoring, token management, and prompt optimization. Knowledge of CI/CD pipelines (Jenkins, GitHub Actions) for AI model deployment. Strong understanding of AI security principles, including data privacy and governance considerations.

Curious about the role? What your typical day would look like? 6+ years of relevant DS experience Proficient in a structured Python Proficient in any one of cloud technologies is mandatory (AWS/ Azure/GCP) Follows good software engineering practices and has an interest in building reliable and robust software Good understanding of DS concepts and DS model lifecycle Working knowledge of Linux or Unix environments ideally in a cloud environment Working knowledge of Spark/ PySpark is desirable Model deployment / model monitoring experience is mandatory CI/CD pipeline creation is good to have Excellent written and verbal communication skills

Build and deploy scalable ML models and MLOps pipelines in collaboration with data scientists Required Candidate profile 6–12 yrs in ML development, Python, model tuning, and enterprise AI deployment.

**We are looking for an AI Advisor with 10+ years of experience, based in Bangalore.** Key Responsibilities: Strategic AI Guidance : Advise the leadership and project teams on the responsible and effective integration of AI across engagements. Provide deep insights into the AI and GenAI model landscape, including open-source and commercial offerings. Assess risks related to AI implementation (e.g., bias, misuse, data privacy) and develop mitigation strategies. Model Evaluation & Use Case Realization Support model selection and evaluation for specific use cases, especially in low-resource or domain-specific contexts (e.g., agriculture, governance). Offer guidance on data strategies for model fine-tuning, including training data sufficiency, preprocessing, and adaptation. Work closely with technical teams to help translate domain needs into technical requirements, and vice versa. Help conceptualize and refine real-world use cases from ideation to implementation, including AI workflows and impact pathways. Cross-Functional Collaboration Engage with ecosystem of partners governments, development agencies, academic institutionsto drive AI thinking across projects. Communicate complex AI concepts clearly to non-technical stakeholders, enabling better alignment and decision-making. Collaborate with cross-functional teams to define requirements for AI components in DPGs and platforms. Ethical AI & Data Governance Ensure all AI solutions adhere to ethical AI principles, including fairness, transparency, explainability, and accountability. Provide strategic inputs on data governance, especially in contexts involving sensitive or multilingual datasets. Align recommendations with emerging AI regulations and standards, both global and India-specific. Qualifications & Skills: Bachelors or Masters degree in a relevant field (e.g., Computer Science, Data Science, AI, NLP, or related). 10+ years of experience in AI, consulting, or technology roles, with a strong foundation in language technology and NLP. Proven ability to evaluate and fine-tune models, especially in low-resource or emerging domain contexts. Strong understanding of AI model lifecycles, including data sourcing, model training, validation, deployment, and feedback. Excellent communication skills and experience working with multi-stakeholder environments, especially in public sector or mission-driven settings Familiarity with data privacy frameworks, ethical AI standards, and responsible AI deployment practices. Ability to think strategically, act hands-on, and operate independently in a fast-moving, collaborative environment.

Location: PAN India Immediate joiner required Experienced in end to end capital or Impairment process management RWA calculation engine using Standardized approach (BASEL 1/2/3) for Credit Risk Asset classification for Retail and Wholesale Products, Collateral Adjustment, on balance/ off balance exposure calculation Migration RWA calculation from BASEL 3 to 3.1 using Standardized or AIRB approach Knowledge of Banking domain/ banking products like Retail, corporate, banks, SME, Sovereign Project management / stakeholder management Keywords : Standardized approach - BASEL 1/2/3 Capital Reporting (Standardized approach) RWA Calculation Credit Risk (RWA) capital management

MLE/Sr. MLE Chennai, Bangalore, Hyderabad Who we are Tiger Analytics is a global leader in AI and analytics, helping Fortune 1000 companies solve their toughest challenges. We offer full-stack AI and analytics services & solutions to empower businesses to achieve real outcomes and value at scale. We are on a mission to push the boundaries of what AI and analytics can do to help enterprises navigate uncertainty and move forward decisively. Our purpose is to provide certainty to shape a better tomorrow. Our team of 4000+ technologists and consultants are based in the US, Canada, the UK, India, Singapore and Australia, working closely with clients across CPG, Retail, Insurance, BFS, Manufacturing, Life Sciences, and Healthcare. Many of our team leaders rank in Top 10 and 40 Under 40 lists, exemplifying our dedication to innovation and excellence. We are a Great Place to Work-Certified (2022-25), recognized by analyst firms such as Forrester, Gartner, HFS, Everest, ISG and others. We have been ranked among the Best and Fastest Growing analytics firms lists by Inc., Financial Times, Economic Times and Analytics India Magazine. Curious about the role? What your typical day would look like? We are looking for a Machine Learning Engineer/Sr MLE who will work on a broad range of cutting-edge data analytics and machine learning problems across a variety of industries. More specifically, you will Engage with clients to understand their business context. Translate business problems and technical constraints into technical requirements for the desired analytics solution. Collaborate with a team of data scientists and engineers to embed AI and analytics into the business decision processes. What do we expect? 6+ years of experience with at least 4+ years of relevant MLOps experience. Proficient in a structured Python (Mandate) Proficient in Azure Databricks Follows good software engineering practices and has an interest in building reliable and robust software. Good understanding of DS concepts and DS model lifecycle. Working knowledge of Linux or Unix environments ideally in a cloud environment. Working knowledge of Spark/ PySpark is desirable. Model deployment / model monitoring experience is desirable. CI/CD pipeline creation is good to have. Excellent written and verbal communication skills. B.Tech from Tier-1 college / M.S or M. Tech is preferred. You are important to us, lets stay connected! Every individual comes with a different set of skills and qualities so even if you dont tick all the boxes for the role today, we urge you to apply as there might be a suitable/unique role for you tomorrow. We are an equal-opportunity employer. Our diverse and inclusive culture and values guide us to listen, trust, respect, and encourage people to grow the way they desire. Note: The designation will be commensurate with expertise and experience. Compensation packages are among the best in the industry. Additional Benefits: Health insurance (self & family), virtual wellness platform, and knowledge communities.

TensorFlow, PyTorch Multi-GPU/TPU, distributed data pipelines Advanced techniques, RAG systems AWS, GCP, or Azure Terraform, Airflow, Kubeflow Prometheus, Grafana, or similar tools Version Control & CI/CD: Git, Jenkins, GitHub Actions, etc

Min exp 3 years in pd. lgd MODELS. IFRS9/ IRB model validation/ development/monitoring exp mandatory Package upto 55 lpa Depends on exp Required Candidate profile Bangalore/Noida location Please send cv's on supreetbakshi@imaginators.co or Call on 7042331616 Please send cv's on email only