Data-AI/ML Engineer

1. BASIC ROLE DETAILS
Position Title Data-AI/ML Engineer Grade: JE is to be done
Business Group / Division/ Department Research & Development
Location Thane (To align with the Head's location)
This Role Reports to Head AI/ML Technologies/ Senior Data Scientist
This Role Supervises -

2. PURPOSE OF THE ROLE
To support the industrialization of AI/ML solutions by focusing on the development, integration, and maintenance of machine learning infrastructure and deployment pipelines. This role is crucial for ensuring that models developed by Data Scientists are successfully and reliably moved from experimentation to production environments.
Role Position in the Organization (enclose a copy of the organization chart)


3. IDEAL PROFILE
Educational Qualification Essential Bachelor's Engineering degree
Preferred Bachelor's degree in Computer Science, Engineering, or a related field with a focus on machine learning or software development
Total Experience Required 2+ Yrs Relevant Experience 0-2 Yrs
Functional / Technical Expertise • Programming & Software Engineering: Strong proficiency in Python and solid software development fundamentals. Experience with object-oriented programming.

• MLOps & Deployment: Foundational understanding of the machine learning lifecycle and basic MLOps practices. Familiarity with model packaging, versioning, and testing.
• Cloud & Infrastructure: Basic experience with at least one cloud platform (Azure preferred, AWS, or GCP) and cloud services relevant to ML (e.g., storage, compute).
• Data & Pipelines: Familiarity with SQL and concepts of data streaming technologies (e.g., Kafka, MQTT). Exposure to building simple ETL/data ingestion pipelines.
• ML Model Support: Working knowledge of ML libraries (scikit-learn, TensorFlow or PyTorch). Ability to integrate pre-trained models into application code.
• Version Control: Proficiency in using Git for version control.

Preferred expertise

• Azure Exposure: Academic or project experience with Azure Machine Learning workspace or Azure DevOps for CI/CD.
• Embedded Systems: Basic awareness of embedded product development or IoT sensor data.

Behavioral / Leadership Competency • Strong self-drive

• Eagerness to learn
• High attention to detail
• Effective collaboration within a technical team
• Strong Communication

MAIN RESPONSIBILITIES
Key Deliverables for this position

• Pipeline Development: Assist in building and maintaining automated ML pipelines for data processing, model training, and re-training.
• Model Deployment Support: Support the team in deploying and integrating trained ML models (e.g., time series, agentic) as services or endpoints for use by product software and applications.
• MLOps Execution: Implement and adhere to basic MLOps practices for model versioning, testing, and monitoring, ensuring reliable deployment.
• Data Handling: Write and optimize code for data ingestion, cleaning, and transformation necessary for model consumption.
• Quality & Traceability: Support processes to achieve functional specification traceability for new AI-enabled features.
• Infrastructure Support: Maintain documentation for the ML infrastructure and deployment procedures.

Key Performance Indicators

• Deployment Success Rate: Percentage of models successfully deployed into staging environments.
• Pipeline Efficiency: Time taken to execute core ML pipeline steps.
• Technical Documentation: Quality and completeness of MLOps and deployment documentation.

5.
KEY INTERACTIONS
With Whom Brief Description
External • External consultants, if deployed

• Project consultants
• Software providers

Internal • Program & Project managers

• System integrators
• Electronics & testing engineers

6. WORKING CONDITIONS (PHYSICAL & ENVIRONMENTAL DEMAND)
Office based environment and Regular travel to various R&D office locations and factories

7. AUTHORITY - INDICATE THE APPROVAL AUTHORITY FOR THIS POSITION, IF ANY.
People
Financial

8. ACCOUNTABILITY & CHALLENGES
Key Financial Accountabilities •
Major Challenges • Translating business problems into practical data science solutions and delivering production-ready, measurable models.

• Coordinating with cross-functional teams (System Integration, Quality, Electronics) for the smooth release of complex, integrated products.
• Maintaining data Integrity