Job/Position SummaryResponsibilities:- Own end-to-end MLOps delivery for ML solutions, from model packaging and validation to deployment, monitoring, and lifecycle management.
- Build and maintain scalable data and ML pipelines using PySpark, Spark, and Azure Databricks.
- Package, version, and deploy machine learning models developed in Databricks to Azure cloud services and edge devices.
- Design and implement cloud-to-edge ML deployment workflows, including model promotion, artifact management, rollback, and remote update strategies.
- Use MLflow for experiment tracking, model registry, model versioning, and deployment governance.
- Build CI/CD pipelines for ML workloads using tools such as Azure DevOps, GitHub Actions, or similar platforms.
- Containerize ML models and inference services using Docker and deploy them to edge environments.
- Support deployment to edge platforms such as Azure IoT Edge, Kubernetes, K3s, Docker runtime, or embedded Linux based devices.
- Optimize models for edge inference, including latency, memory footprint, CPU/GPU utilization, startup time, and reliability.
- Collaborate with data scientists and ML engineers to convert trained models into production-ready inference services.
- Implement model evaluation, validation, and release gates before cloud or edge deployment.
- Monitor models in production for performance, drift, data quality, system failures, and edge-device health.
- Build logging, observability, and alerting mechanisms for deployed ML services across cloud and edge environments.
- Troubleshoot deployment, runtime, connectivity, and performance issues across cloud and edge systems.
- Work independently on ambiguous technical problems and convert them into scalable, maintainable production solutions.
- Collaborate with data engineers, cloud engineers, software engineers, infrastructure teams, and business stakeholders.
- Guide and support team members on MLOps, model deployment, production ML, and cloud-to-edge best practices.
- Write clean, production-ready, well-tested, and well documented code.
Requirements:- Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, Electrical Engineering, or a related field.
- Equivalent practical experience will also be considered.
- 5+ years of hands-on experience in MLOps, machine learning engineering, applied ML, AI engineering, data science, or production software engineering.
- Proven experience deploying ML or AI solutions into production environments.
- Hands-on experience working with Azure Databricks and Azure cloud environments.
- Experience building or supporting CI/CD pipelines for ML or software systems.
- Experience deploying, monitoring, and maintaining ML models in production.
- Experience with cloud-to-edge, IoT, embedded, or distributed deployment environments is strongly preferred.
- Strong hands-on experience with Python and SQL.
- Experience with PySpark, Spark, and Azure Databricks.
- Strong working knowledge of Azure cloud services and cloud native deployment patterns.
- Hands-on experience with MLflow for experiment tracking, model registry, model packaging, and model lifecycle management.
- Strong understanding of MLOps fundamentals, including CI/CD, model versioning, automated testing, release management, monitoring, and rollback.
- Experience deploying ML models as production inference services using REST APIs, batch inference, streaming inference, or containerized services.
- Experience with Docker and container-based deployment.
- Experience with Azure DevOps, GitHub Actions, Jenkins, or similar CI/CD tools.
- Familiarity with Azure IoT Edge, edge gateways, embedded Linux, Kubernetes, K3s, or container runtimes on edge devices.
- Understanding of edge deployment challenges such as limited compute, memory constraints, intermittent connectivity, offline inference, remote updates, and device fleet management.
- Ability to optimize ML models for edge inference using approaches such as model compression, quantization, ONNX conversion, or runtime optimization.
- Good understanding of ML workflows, model development, validation, evaluation, and production deployment.
- Experience with production monitoring, logging, observability, drift detection, and model performance tracking.
- Ability to build end-to-end data and ML solutions that are reliable, scalable, and maintainable.
- Understanding of data engineering, feature engineering, and data modeling basics.
- Ability to work independently on loosely defined problems.
- Strong problem-solving, communication, and stakeholder management skills.
- Experience mentoring or guiding engineers is a plus.
Preferred / Nice-to-Have Skills:- Experience deploying models to industrial edge devices, manufacturing systems, connected products, IoT gateways, or embedded platforms.
- Experience with Azure IoT Hub, Azure IoT Edge, Azure Container Registry, Azure Kubernetes Service, Azure Machine Learning, or Azure Monitor.
- Experience with real-time or near-real-time inference on edge devices.
Compensation and Benefits:Along with competitive pay, as a full-time KPIT employee, you are eligible for the following benefits:
- Geo Blue PPO and HSA plan.
- MetLife - Dental and Vision plan.
- Healthcare and Dependent care flexible spending account(FSA).
- 401k with employer match.
- Company-paid Basic Life and Long-term disability insurance.
- Voluntary benefits include Critical Illness, Hospital indemnity, accident insurance, theft, and legal service.
- Employee Assistance Program.
- Paid Holidays.
- Employee discounts and perks.
- Gym benefit.
ESSENTIAL SKILLS /COMPETENCIES• MLOPS
• MACHINE LEARNING
• AI ENGINEERING
• DATA SCIENCE
• AZURE DATABRICKS
• AZURE CLOUD
• CI/CD
PREFFERED SKILLS /COMPETENCIES• ,"description":"
Responsibilities: - Own end-to-end MLOps delivery for ML solutions
• from model packaging and validation to deployment
• monitoring
• and lifecycle management. - Build and maintain scalable data and ML pipelines using PySpark
• Spark
• and Azure Databricks. - Package
• version
• and deploy machine learning models developed in Databricks to Azure cloud services and edge devices. - Design and implement cloud-to-edge ML deployment workflows
• including model promotion
• artifact management
• rollback
• and remote update strategies. - Use MLflow for experiment tracking
• model registry
• model versioning
• and deployment governance. - Build CI/CD pipelines for ML workloads using tools such as Azure DevOps
• GitHub Actions
• or similar platforms. - Containerize ML models and inference services using Docker and deploy them to edge environments.
- Support deployment to edge platforms such as Azure IoT Edge
• Kubernetes
• K3s
• Docker runtime
• or embedded Linux based devices. - Optimize models for edge inference
• including latency
• memory footprint
• CPU/GPU utilization
• startup time
• and reliability. - Collaborate with data scientists and ML engineers to convert trained models into production-ready inference services.
- Implement model evaluation
• validation
• and release gates before cloud or edge deployment. - Monitor models in production for performance
• drift
• data quality
• system failures
• and edge-device health. - Build logging
• observability
• and alerting mechanisms for deployed ML services across cloud and edge environments. - Troubleshoot deployment
• runtime
• connectivity
• and performance issues across cloud and edge systems. - Work independently on ambiguous technical problems and convert them into scalable
• maintainable production solutions. - Collaborate with data engineers
• cloud engineers
• software engineers
• infrastructure teams
• and business stakeholders. - Guide and support team members on MLOps
• model deployment
• production ML
• and cloud-to-edge best practices. - Write clean
• production-ready
• well-tested
• and well documented code.
Requirements: - Bachelor's or Master's degree in Computer Science
• Data Science
• Engineering
• Mathematics
• Statistics
• Electrical Engineering
• or a related field. - Equivalent practical experience will also be considered.
- 5+ years of hands-on experience in MLOps
• machine learning engineering
• applied ML
• AI engineering
• data science
• or production software engineering. - Proven experience deploying ML or AI solutions into production environments.
- Hands-on experience working with Azure Databricks and Azure cloud environments.
- Experience building or supporting CI/CD pipelines for ML or software systems.
- Experience deploying
• monitoring
• and maintaining ML models in production. - Experience with cloud-to-edge
• IoT
• embedded
• or distributed deployment environments is strongly preferred. - Strong hands-on experience with Python and SQL.
- Experience with PySpark
• Spark
• and Azure Databricks. - Strong working knowledge of Azure cloud services and cloud native deployment patterns.
- Hands-on experience with MLflow for experiment tracking
• model registry
• model packaging
• and model lifecycle management. - Strong understanding of MLOps fundamentals
• including CI/CD
• model versioning
• automated testing
• release management
• monitoring
• and rollback. - Experience deploying ML models as production inference services using REST APIs
• batch inference
• streaming inference
• or containerized services. - Experience with Docker and container-based deployment.
- Experience with Azure DevOps
• GitHub Actions
• Jenkins
• or similar CI/CD tools. - Familiarity with Azure IoT Edge
• edge gateways
• embedded Linux
• Kubernetes
• K3s
• or container runtimes on edge devices. - Understanding of edge deployment challenges such as limited compute
• memory constraints
• intermittent connectivity
• offline inference
• remote updates
• and device fleet management. - Ability to optimize ML models for edge inference using approaches such as model compression
• quantization
• ONNX conversion
• or runtime optimization. - Good understanding of ML workflows
• model development
• validation
• evaluation
• and production deployment. - Experience with production monitoring
• logging
• observability
• drift detection
• and model performance tracking. - Ability to build end-to-end data and ML solutions that are reliable
• scalable
• and maintainable. - Understanding of data engineering
• feature engineering
• and data modeling basics. - Ability to work independently on loosely defined problems.
- Strong problem-solving
• communication
• and stakeholder management skills. - Experience mentoring or guiding engineers is a plus.
Preferred / Nice-to-Have Skills: - Experience deploying models to industrial edge devices
• manufacturing systems
• connected products
• IoT gateways
• or embedded platforms. - Experience with Azure IoT Hub
• Azure IoT Edge
• Azure Container Registry
• Azure Kubernetes Service
• Azure Machine Learning
• or Azure Monitor. - Experience with real-time or near-real-time inference on edge devices.
Compensation and Benefits: Along with competitive pay
• as a full-time KPIT employee
• you are eligible for the following benefits:
- Geo Blue PPO and HSA plan.
- MetLife - Dental and Vision plan.
- Healthcare and Dependent care flexible spending account(FSA).
- 401k with employer match.
- Company-paid Basic Life and Long-term disability insurance.
- Voluntary benefits include Critical Illness
• Hospital indemnity
• accident insurance
• theft
• and legal service. - Employee Assistance Program.
- Paid Holidays.
- Employee discounts and perks.
- Gym benefit.
