Machine Learning (ML) Engineer (CE60SF RM 3571)

Position: Machine Learning (ML) Engineer (CE60SF RM 3571)

Shift timing : General Shift / 5 days week work from office
Relevant Experience required : 4+ years
Education Required : Bachelor’s / Masters / PhD : B.E Computers, MCA is preferable

Must have skills:

Machine Learning

Python

DataBricks

Cloud Platform with strong MLOps exposure [Azure preferable]

SQL, NoSQL, and data modeling

Drift Detection & Monitoring

Data Governance

Good to have:

LLM solutions (RAG, Agentic AI, MCP, prompt engineering).

Power BI dashboards

Role Summary
Lead the design, development, and deployment of ML solutions at scale. Drive architecture, mentor the team, and integrate advanced AI (including LLMs) into enterprise workflows.

Note: Deep Learning is GREAT to have but Machine Learning is MANDATORY

Must-Have (Mandatory)

Machine Learning: Deep understanding of supervised, unsupervised, and reinforcement learning, model evaluation, and feature engineering.

Deep Learning: Proficiency with TensorFlow, PyTorch, Keras; hands-on with CNNs, RNNs.

Programming: Expert in Python (NumPy, Pandas, scikit-learn, etc.); R exposure acceptable.

Big Data Technologies: Practical experience with Spark (Databricks preferred); familiarity with Hadoop/Kafka.

Cloud Platforms: Azure or AWS or GCP (ML services, data storage, compute), with strong MLOps exposure.

Data Warehousing & Databases: Strong SQL, NoSQL, and data modeling.

Drift Detection & Monitoring: Hands-on experience with model drift detection, monitoring, and automated alerts.

Data Governance: Practical experience implementing governance frameworks, lineage tracking, metadata management, and compliance.

Architect scalable MLOps pipelines using Azure ML, MLflow, Databricks Asset Bundles (DAB), and CI/CD/CT

Good-to-Have:

Design & deploy LLM solutions (RAG, Agentic AI, MCP, prompt engineering).

Build Power BI dashboards for monitoring models and reporting business KPIs.

Strong grounding in statistics, hypothesis testing, and data interpretation.

Apply software engineering principles for reusable, testable, and maintainable ML code.

Stay up to date with Generative AI, Agentic AI, and LLMs and assess practical adoption.

Mentor juniors, review code, and conduct technical knowledge-sharing sessions.

Certification: Microsoft Certified: Azure Data Scientist Associate (nice to have).

*******************************************************************************************************************************************