Senior Machine Learning Engineer - Data Analytics

While technology is the heart of our business, a global and diverse culture is the heart of our success. We love our people and we take pride in catering them to a culture built on transparency, diversity, integrity, learning and growth.


If working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi!

Role : Senior Machine Learning Engineer - Data Analytics

Experience : 3-5 Years

Location : Bangalore (Hybrid)

Role & Responsibilities:

• 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

Must have skills:

• 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

Good to have skills:

• AWS Bedrock Models

• Redshift and SQL

• ML Algos : Bagging and Boosting algorithms

• Knowledge of Data Pipelines (GlueJobs)