Senior Machine Learning Engineer - Data Analytics

Quantiphi · Bangalore

Source and collection record

Last observed by our collector: 8 October 2026 at 10:50 pm IST. This is not a manual review or a confirmation from the employer.

Employer requisition: JR9600

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Collected role description

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) If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us !

Collected experience requirement

3 years