120 Ml Deployment Jobs

We are hiring for Python Developer ( PySpark ML, Snowpark ML, Snowflake ML deployment experience) Experience: 5+ years Notice Period: Immediate to 30 days Job Summary A Data Scientist job description typically outlines the role's responsibilities, which include collecting, analysing, and interpreting data to extract insights that can inform business decisions and improve operations. Data Scientists use programming, statistical analysis, and machine learning techniques to uncover patterns, build predictive models, and communicate to Stakeholders. Candidates must demonstrate proficiency in script-based solutions. Key Responsibilities: Data scientists use statistical methods to collect and orga...

Role Description : As a Senior ML Engineer / ML Ops - GCP Specialist at Incedo, you will be responsible for building and deploying machine learning models in production environments You will work with data scientists, data engineers, and software developers to design and implement machine learning pipelines You will be skilled in programming languages such as Python or Java and have experience in ML tools such as TensorFlow or PyTorch You will be responsible for ensuring that models are scalable, efficient, and can be deployed in production environments Roles & Responsibilities: Designing and developing machine learning (ML) models and algorithms Implementing and maintaining ML pipelines and...

Role Purpose The purpose of the role is to define, architect and lead delivery of machine learning and AI solutions Do 1. Demand generation through support in Solution development a. Support Go-To-Market strategy i. Collaborate with sales, pre-sales &consulting team to assist in creating solutions and propositions for proactive demand generation ii. Contribute to development solutions, proof of concepts aligned to key offerings to enable solution led sales b. Collaborate with different colleges and institutes for recruitment, joint research initiatives and provide data science courses 2. Revenue generation through Building & operationalizing Machine Learning, Deep Learning solutions a. Devel...

Role Description As a Senior ML Engineer / ML Ops - GCP Specialist at Incedo, you will be responsible for building and deploying machine learning models in production environments You will work with data scientists, data engineers, and software developers to design and implement machine learning pipelines You will be skilled in programming languages such as Python or Java and have experience in ML tools such as TensorFlow or PyTorch You will be responsible for ensuring that models are scalable, efficient, and can be deployed in production environments Roles & Responsibilities: Designing and developing machine learning (ML) models and algorithms Implementing and maintaining ML pipelines and w...

Role Description As a Senior ML Engineer / ML Ops - GCP Specialist at Incedo, you will be responsible for building and deploying machine learning models in production environments You will work with data scientists, data engineers, and software developers to design and implement machine learning pipelines You will be skilled in programming languages such as Python or Java and have experience in ML tools such as TensorFlow or PyTorch You will be responsible for ensuring that models are scalable, efficient, and can be deployed in production environments Roles & Responsibilities: Designing and developing machine learning (ML) models and algorithms Implementing and maintaining ML pipelines and w...

Role Description 1. AI Product Strategy & ExecutionDefine and drive the product vision, roadmap, and execution strategy for AI-powered solutions, with a focus on computer vision technologies and real-time AI models.Own key product KPIs, including model accuracy, latency, adoption rate, and computational efficiency for cloud/edge deployments.Lead the end-to-end product lifecycle, from conceptualization and prototyping to large-scale deployment.Ensure scalability, modularity, and reusability of AI & Engineering components across different use cases, by working closely with Engineering and AI scientist team .Optimize AI model performance for edge devices, low-latency processing, and cloud-scale...

Job Title: Machine Learning Advisor Location: Remote Position Type: Part-Time (20 hours/week) Experience Level: Senior (8 to 10+ years) About the Role: We are looking for a hands-on Machine Learning Advisor who can both build initial models and mentor our junior engineers as they refine and productionize them. This is a part-time role designed for a senior professional who enjoys rolling up their sleeves to prototype and code , while also providing strategic guidance, architecture oversight, and mentorship .You will help us establish a solid ML foundation by: Building and validating the first versions of our models . Setting up the right processes, frameworks, and best practices. Working clo...

Role & responsibilities Job Title: Machine Learning Operations Engineer / MLOps Engineer, Place of Work: Mumbai / Bangalore. Function/ Department: Data & Analytics. Years of Experience: 6+ years Job Purpose : 'As an MLOps Engineer, you will be responsible for deploying, scaling, and monitoring ML & GenAI models in production. You will own the infrastructure, automation, and observability layer while collaborating with ML Engineers and Data Scientists to ensure models (LLMs, embeddings, recommendation systems, etc.) are reliably served at scale. This role combines DevOps best practices with ML-specific needs, ensuring smooth model lifecycle management and robust GenAI-powered applications. Ke...

Strong in Python (Pandas, NumPy, Scikit-learn, PyTorch, TensorFlow). Hands-on experience with ML Ops frameworks/tools Experience with containerization & orchestration. Strong knowledge of CI/CD tools (GitHub, etc.) Experience in AWS / Azure. .

We are seeking a Machine Learning Engineer with strong expertise in traditional ML algorithms and statistical modeling techniques. The role involves developing, training, testing, and deploying ML models to solve business problems, improve decision-making, and optimize processes. The ideal candidate should have a solid background in mathematics, statistics, and programming, with hands-on experience in applying classical ML techniques rather than relying solely on deep learning frameworks. Key Responsibilities: Design, develop, and implement machine learning models using traditional algorithms (e.g., regression, SVM, decision trees, ensemble methods, clustering, time series forecasting). Perf...

Role Purpose The purpose of the role is to define, architect and lead delivery of machine learning and AI solutions. Do 1. Demand generation through support in Solution development a. Support Go-To-Market strategy i. Contribute to development solutions, proof of concepts aligned to key offerings to enable solution led sales b. Collaborate with different colleges and institutes for research initiatives and provide data science courses 2. Revenue generation through Building & operationalizing Machine Learning, Deep Learning solutions a. Develop Machine Learning / Deep learning models for decision augmentation or for automation solutions b. Collaborate with ML Engineers, Data engineers and IT t...

Responsibilities: * Develop computer vision solutions using Python, PyTorch or TensorFlow. * Collaborate with cross-functional teams on ML pipeline development and deployment.

Modeling & Analysis: Building, training, and validating models Cleaning and preparing data Combining and shaping data Applying descriptive and inferential statistics Azure Services & Tools : Use of Azure Notebooks, Azure Machine Learning, Azure Databricks, and Azure Synapse Analytics Integration with Kusto Query Language and Azure Data Explorer Machine Learning & Feature Engineering : Preparing data for ML in Azure Building features from nominal, text, and numeric data in Azure Developing and deploying ML models using Azure services Integration with Open-Source Tools : Azure supports Python, R, TensorFlow, PyTorch, scikit-learn, pandas, and NumPy

About the job : Zeta Global is looking for an experienced ML Engineer with industry-proven hands-on experience of delivering machine learning models to production to solve business problems. To be a good fit to join our AI/ML team, you should ideally: Be a thought leader that can work with cross-functional partners to foster a data-driven organization. Be a strong team player, have experience contributing to a large project as part of a collaborative team effort. Have extensive knowledge and expertise with machine learning best-practices and industry standards. Be able to design and execute experiments to measure the impact of new features and machine learning models on business outcomes. Em...

e-Zest, an AccionLabs company , as we are urgently hiring a Machine Learning Engineer for our Pune location. In this role, you will be responsible for designing, developing, and deploying scalable ML models that solve real-world business challenges. Youll work closely with data scientists, engineers, and product teams to deliver production-ready ML solutions with strong performance and scalability. Key Highlights of the Role: Build and deploy ML models & pipelines in production. Collaborate with data scientists to optimize algorithms for real-world use. Work with large-scale datasets for training & evaluation. Implement MLOps practices (CI/CD, model versioning, automated deployment). Develop...

5+ Years of prior experience in Data Engineering and MLOPs. 3+ Years of strong exposure in deploying and managing data science pipelines in production environments. Strong proficiency in Python programming language. Experience with Spark/PySpark and distributed computing frameworks. Hands-on experience with CI/CD pipelines and automation tools. Exposure in deploying a use case in production leveraging Generative AI involving prompt engineering and RAG Framework Familiarity with Kafka or similar messaging systems. Strong problem-solving skills and the ability to iterate and experiment to optimize AI model behavior. Excellent problem-solving skills and attention to detail. Ability to communica...

Key Responsibilities Design, fine-tune, and manage prompts for diverse enterprise LLM use cases. Operate, monitor, and troubleshoot agentic AI applications , diagnosing whether issues stem from: Prompt structure/quality Model configuration or performance Tool usage, API failures, or memory/recall issues Build diagnostic playbooks for LLM-driven failures, including fallback strategies, retries, and re-routing to human workflows. Collaborate with AI architects, ML engineers, and DevOps teams to optimize agent orchestration across platforms such as LangGraph, CrewAI, AutoGen, etc. Support integration of agentic systems with enterprise tools (Jira, ServiceNow, Glean, Confluence) via REST APIs, w...

Skills Needed : Deploying ML models in cloud (AWS, Azure, Oracle) and on-premises. CI/CD pipelines for ML. Containerization (Docker, Kubernetes). Building and maintaining data pipelines . Model monitoring and retraining. About the role: MLOps professional with 6-8 years of experience in deploying and scaling machine learning models in production environments of banks. Should be thrive at the intersection of data science and engineering, ensuring that fraud detection models move seamlessly from prototype to production. Hands-on experience with cloud platforms (AWS, Azure, Oracle) as well as on-premise deployments, and should be comfortable working with containerization, orchestration, and CI/...

We are seeking a skilled ML Ops Engineer to join our team and streamline the deployment, monitoring, and management of machine learning models at scale. You will collaborate closely with data scientists, engineers, and DevOps to build robust, scalable ML infrastructure and CI/CD pipelines for AI/ML workflows. ML Ops engineer/ML engineer Project Description The engineer is supposed to participate in various AI projects such as Demand Sensing and Forecasting, Price and Promotion Optimization and others. Details on Tech Stack Proficiency in Python. Competent knowledge of best practices for software development. Strong understanding of Data Science concepts such as supervised and unsupervised le...

We are seeking a skilled ML Ops Engineer to join our team and streamline the deployment, monitoring, and management of machine learning models at scale. You will collaborate closely with data scientists, engineers, and DevOps to build robust, scalable ML infrastructure and CI/CD pipelines for AI/ML workflows. ML Ops engineer/ML engineer Project Description The engineer is supposed to participate in various AI projects such as Demand Sensing and Forecasting, Price and Promotion Optimization and others. Details on Tech Stack Proficiency in Python. Competent knowledge of best practices for software development. Strong understanding of Data Science concepts such as supervised and unsupervised le...

We are seeking a skilled ML Ops Engineer to join our team and streamline the deployment, monitoring, and management of machine learning models at scale. You will collaborate closely with data scientists, engineers, and DevOps to build robust, scalable ML infrastructure and CI/CD pipelines for AI/ML workflows. ML Ops engineer/ML engineer Project Description The engineer is supposed to participate in various AI projects such as Demand Sensing and Forecasting, Price and Promotion Optimization and others. Details on Tech Stack Proficiency in Python. Competent knowledge of best practices for software development. Strong understanding of Data Science concepts such as supervised and unsupervised le...

dunnhumby is the global leader in Customer Data Science, empowering businesses everywhere to compete and thrive in the modern data-driven economy. We always put the Customer First. Our mission: to enable businesses to grow and reimagine themselves by becoming advocates and champions for their Customers. With deep heritage and expertise in retail one of the worlds most competitive markets, with a deluge of multi-dimensional data – dunnhumby today enables businesses all over the world, across industries, to be Customer First. dunnhumby employs nearly 2,500 experts in offices throughout Europe, Asia, Africa, and the Americas working for transformative, iconic brands such as Tesco, Coca-Cola, Me...

Role & responsibilities Design and manage end-to-end ML pipelines for deployment and monitoring. Automate model training, retraining, and deployment workflows . Implement CI/CD for ML models to ensure reliability and scalability. Build real-time dashboards and APIs for model outputs and business consumption. Optimize cloud resources (Azure Databricks, ADF, Azure ML) for performance and cost efficiency. End-to-end model lifecycle management from development to deployment. Collaborate with data scientists, engineers, and business teams to operationalize ML solutions. Preferred candidate profile 3+ years of experience in MLOps / Data Science and model deployment . Strong skills in Python, PySpa...

Role Purpose The purpose of the role is to define, architect and lead delivery of machine learning and AI solutions Do 1. Demand generation through support in Solution development a. Support Go-To-Market strategy i. Collaborate with sales, pre-sales &consulting team to assist in creating solutions and propositions for proactive demand generation ii. Contribute to development solutions, proof of concepts aligned to key offerings to enable solution led sales b. Collaborate with different colleges and institutes for recruitment, joint research initiatives and provide data science courses 2. Revenue generation through Building & operationalizing Machine Learning, Deep Learning solutions a. Devel...

Role Purpose The purpose of the role is to define, architect and lead delivery of machine learning and AI solutions. Do 1. Demand generation through support in Solution development a. Support Go-To-Market strategy i. Contribute to development solutions, proof of concepts aligned to key offerings to enable solution led sales b. Collaborate with different colleges and institutes for research initiatives and provide data science courses 2. Revenue generation through Building & operationalizing Machine Learning, Deep Learning solutions a. Develop Machine Learning / Deep learning models for decision augmentation or for automation solutions b. Collaborate with ML Engineers, Data engineers and IT t...