Senior Machine Learning Engineer - Data Analytics

Experimenting with range of models, evaluating model performance and model selection.

Performing data cleaning, feature engineering, selection and evaluation.

Implementing the data and model training pipelines on cloud using AWS services such as sagemaker, lambda functions, etc.

Documentation for Model architecture and solutions

Collaboration with cross-functional teams, including platform engineers, Machine learning engineers, software developers and business stakeholders, to ensure data solutions meet business needs.

Adhering to project timelines

Communicate with non-technical stakeholders to understand their data requirements and convey the benefits of data solutions, including migration strategies

Machine Learning Engineer with 3 4 years of experience, based in Bangalore, with a requirement to work from the client s office 2 days a week.

Good exposure on Python (Pandas, Numpy, Matplotlib, Advance Python Syntax s etc)

Hands on experience on OpenAI Framework, required to develop AI applications.

Handson experience in developing the RAG pipeline, LLM Gen AI models and Prompt Engineering.

Handover experience on creating the MCP s (Model Context Protocol).

Exposure on Agentic frameworks like langgraph and langchain.

Exposure to the Agentic framework (like AWS Bedrock Agentcore) is mandatory.

Exposure on Data Analytics - Data Analytics, Advanced SQL and Amazon Redshift , AWS Glue , Amazon DynamoDB , Amazon Managed Streaming for Apache Kafka.

Exposure on below AWS Services - Amazon Bedrock (AgentCore), Amazon SageMaker Studio , Amazon Elastic Container Registry , Amazon API Gateway , AWS Elastic Beanstalk , AWS Lambda , Amazon Elastic Container Service , Kubernetes.

Hands-on GenAI Model Providers (example : OpenAI models, Anthropic models and Gemini Models).

ML Algos : Bagging and Boosting algorithms

AWS Bedrock Models

Redshift and SQL

ML Algos : Bagging and Boosting algorithms

Knowledge of Data Pipelines (GlueJobs)