Senior Machine Learning Engineer

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

Experience Level: 3 to 8 Years

Work location: Mumbai/ Bengaluru/ Trivandrum

What you'll do:

As an Senior Machine Learning Engineer in the Healthcare & Life Sciences (HCLS) unit at Quantiphi, you will be a key technical leader responsible for the end-to-end execution of complex AI/ML projects. This is a highly hands-on architectural role where you will spend 50% to 75% of your time writing production-grade code, building prototypes, and designing system components.

You will act as the technical anchor for your project team, translating high-level architecture designs into robust, scalable, and deployable implementations. You will mentor senior and junior engineers, lead technical reviews, and engage directly with clients to drive updates, manage technical risks, and clearly explain architectural trade-offs using structured visual representations and deep technical reasoning.

Role & Responsibilities:

• End-to-End Project Delivery: Own the technical delivery of a project from an ML standpoint. Lead the implementation, deployment, and operationalization of ML, Deep Learning, NLP, and Generative AI solutions.

• Hands-on Development: Spend 50% to 75% of your time coding. Build robust pipelines, develop advanced agentic workflows, and implement core machine learning components in Python and PyTorch/TensorFlow.

• Component-Level Design: Design modular, secure, and scalable AI system components. Create visual system representations (UML, block diagrams, flowcharts) and defend your design choices through rigorous technical reasoning.

• Generative AI & Agentic Workflows: Architect and develop advanced Retrieval-Augmented Generation (RAG) pipelines, implement Agentic AI workflows using multi-agent frameworks, and integrate Model Context Protocol (MCP) servers and clients.

• MLOps/LLMOps Engineering: Design and maintain production-ready MLOps pipelines (CI/CD, automated testing, model registry, monitoring, retraining frameworks, drift detection) on AWS or GCP.

• Technical Mentorship: Code-review and guide senior ML engineers and junior resources, enforcing clean coding standards, modular design patterns, and industry best practices.

• Client Engagement: Lead technical discussions with clients regarding project updates, blockers, and architectural decisions. Translate complex technical concepts into clear business impact.

Must Have Skills:

​Experience: 6 to 8 years of professional experience in Machine Learning, Deep Learning, and Software Engineering, with a proven track record of delivering end-to-end ML projects.

Robust Software Engineering:

• Exceptional mastery of Python (clean, class-based, modular coding) and SQL for processing complex, large-scale datasets.

• Deep understanding of modern software design patterns, Git-based version control, and CI/CD automation.

Advanced ML, DL & NLP:

• Extensive hands-on experience in statistical ML (regression, classification, clustering) and Deep Learning architectures (Transformers, CNNs, RNNs).

• Solid understanding of NLP concepts (syntactic/semantic parsing, text embeddings, tokenization, NER, coreference).

• Generative AI & Agentic Systems (2026 Stack):

• Practical experience designing and deploying Generative AI applications and LLM-based solutions.

• Hands-on implementation of advanced RAG pipelines and familiarity with Vector Databases (e.g., Pinecone, Milvus, Chroma, Qdrant).

• Hands-on experience with Agentic AI Frameworks (e.g., Google ADK, LangChain, LlamaIndex, CrewAI, AutoGen, LangGraph) for autonomous reasoning, planning, and tool use.

• Core understanding of Model Context Protocol (MCP) implementations to manage state, memory, and context windows.

AI System Design & Technical Reasoning:

• Demonstrated ability to design scalable AI pipelines and systems.

• Proficiency in visually diagramming architectures and explaining technical trade-offs with deep, structured reasoning.

Frameworks & MLOps:

• Strong proficiency in PyTorch or TensorFlow.

• Practical experience with MLOps tools (e.g., MLflow, Kubeflow, SageMaker Pipelines, Airflow) and the model lifecycle (feature store, registry, deployment, monitoring).