Machine Learning Engineer

Machine Learning Engineers and ML Leads

ML model development, cloud platforms, and Generative AI tools

Responsibilities:

• Build and maintain scalable

  data & ML pipelines

  using Python, PySpark, and AWS (SageMaker, Bedrock, Kendra).
• Implement

  MLOps best practices

  – CI/CD for ML models, monitoring, versioning, and retraining.
• Work with large datasets, perform

  EDA, feature engineering, and model optimization

  .
• Collaborate with cross-functional teams to deploy ML solutions in production.
• Apply your knowledge of

  Time Series, NLP, and Image/Video Analytics

  to solve real-world problems.
• Explore and integrate

  Generative AI

  ,

  LangChain

  ,

  LlamaIndex

  , and

  RAG

  frameworks into ML workflows.

Skills & Experience Required:

• Strong in

  Python

  and

  Machine Learning

  concepts.
• Hands-on with

  PySpark

  ,

  MLOps

  , and

  AWS Cloud (SageMaker, Bedrock, Kendra)

  .
• Experience with

  model lifecycle management, deployment, and performance metrics

  .
• Exposure to

  Docker, Kubernetes, LangChain, and Generative AI

  preferred.
• Good understanding of

  Statistics, Probability, and Hypothesis Testing

  .

Experience Range:

• ML Engineer: 4–6 years
• ML Module Lead: 6–8 years
• ML Lead: 8–10 years

Locations:

Notice Period:

Skills: Machine Learning

MLOps

Python

PySpark

AWS SageMaker

Bedrock

Kendra

Data Engineering

EDA

NLP

Time Series

Generative AI

LangChain

LlamaIndex

RAG

Docker

Kubernetes

AI Engineer

Machine Learning Engineer