Senior Machine Learning Engineer
Quantiphi (company site, opens in a new tab)4–7 yrsBengaluruOn-siteDirect career pageEst. 20–35 LPA
Posted 17h ago
Reach people at Quantiphi
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Quantiphi you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Design and develop advanced machine learning models and algorithms to address complex business problems.
- Optimize and deploy ML models on AWS infrastructure, ensuring scalability and reliability.
- Develop AI agents and GenAI applications using Agentic AI frameworks and foundation models, including workflow orchestration and secure AI deployment.
Minimum Requirements
- 4-7 years of relevant experience in machine learning engineering.
- Hands-on experience with Agentic AI frameworks (e.g., LangChain, LangGraph, LlamaIndex, CrewAI).
- Proficient in Python programming including Pandas, NumPy, FastAPI, and advanced Python concepts.
- Experience with AWS Bedrock, associated foundation models (Claude Haiku, Claude Sonnet), and AWS services like API Gateway, Lambda, S3, IAM, CloudWatch, ECR, SageMaker.
Ideal Candidate Profile
- Experienced in building RAG pipelines, Prompt Engineering, and vector-based retrieval systems using tools like Titan Embeddings and Amazon OpenSearch Vector Search.
- Skilled in deployment and security aspects of AI applications including AWS Bedrock Guardrails and authorization mechanisms.
- Familiar with software engineering best practices including CI/CD, REST APIs, Git, and preferably has knowledge of OCR/Document Intelligence, MLOps, and container orchestration (Docker, Kubernetes).