Senior Machine Learning Engineer

Role Summary

Looking for a Machine Learning Engineer who can independently own and deliver data and machine learning modules end‑to‑end. This role goes beyond model development — the engineer will handle requirements gathering, data discovery, solution design, pipeline development, deployment, monitoring, and ongoing support. The ideal candidate thrives in ambiguous environments with incomplete requirements and limited documentation, relying on independent investigation and strong engineering judgment.

Responsibilities

Solution Delivery

- Own medium‑to‑high complexity modules from requirement to production
- Translate business and technical requirements into implementable solutions
- Identify missing information, assumptions, dependencies, and risks early
- Drive technical discovery independently without waiting for complete specifications

Data Engineering

- Identify and validate required datasets and source systems
- Perform data profiling, quality analysis, and validation
- Design and build reliable ETL pipelines using AWS services and Python
- Optimize jobs for performance, scalability, maintainability, and cost

Machine Learning

- Develop ML solutions for segmentation, recommendation, personalization, classification, regression, and forecasting
- Select approaches based on data availability, business goals, and operational constraints
- Evaluate trade‑offs between ML and non‑ML approaches
- Build practical, production‑ready solutions

Production Ownership

- Deploy and support delivered modules
- Define monitoring, validation, and failure‑handling strategies
- Troubleshoot production issues and perform root‑cause analysis




- Ensure reproducibility, testability, and maintainability of solutions

Engineering Quality

- Write transparent, maintainable, production‑quality Python code
- Apply robust testing and validation techniques
- Make thoughtful decisions around performance, cost, and reliability
- Review and improve existing implementations

Stakeholder Collaboration

- Work with business users, architects, engineers, and analysts
- Gather information efficiently from documentation, systems, and SMEs
- Communicate assumptions, trade‑offs, risks, and decisions clearly

Required Skills

Strong Python Engineering

- Python, SQL, PySpark
- Data processing and ETL development
- Debugging and troubleshooting

AWS Data Platform

- Hands‑on experience with Amazon S3, AWS Glue, Athena, Lambda, Step Functions, SageMaker, DynamoDB

Data & ML Fundamentals

- Data preparation and feature engineering
- Model evaluation, experimentation, and validation
- Traditional ML techniques

Engineering Judgment

- Work with incomplete requirements and resolve ambiguity
- Evaluate technical trade‑offs
- Optimize compute and infrastructure usage
- Deliver practical solutions within constraints

Preferred Experience

- Recommendation systems
- Personalization platforms
- Customer segmentation
- Customer behavior analytics
- Telecommunications, e‑commerce, or digital product environments
- MLOps and model monitoring

What Success Looks Like Within an assigned module, the engineer should be able to:

- Understand the problem
- Identify and validate required data
- Propose a practical solution
- Build and test it
- Deploy it
- Support it
- Escalate only when architectural or business decisions are required

📌 AI/ML Manager (Bengaluru)
🏢 Innova ESI
📍 Bengaluru