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).
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!
Skills Required
- 6 to 8 years of professional experience in machine learning, deep learning, and software engineering
- Proven track record delivering end-to-end machine learning projects
- Exceptional mastery of Python, including clean, class-based, modular coding
- Advanced SQL experience for processing complex, large-scale datasets
- Strong understanding of modern software design patterns
- Experience with Git-based version control and CI/CD automation
- Hands-on experience with statistical machine learning, including regression, classification, and clustering
- Experience with deep learning architectures including Transformers, CNNs, and RNNs
- Solid understanding of NLP concepts, including parsing, embeddings, tokenization, NER, and coreference
- Practical experience designing and deploying generative AI and LLM-based applications
- Hands-on experience implementing advanced RAG pipelines
- Familiarity with vector databases such as Pinecone, Milvus, Chroma, or Qdrant
- Hands-on experience with agentic AI frameworks such as LangChain, LlamaIndex, CrewAI, AutoGen, LangGraph, or Google ADK
- Core understanding of Model Context Protocol implementations
- Ability to design scalable AI pipelines and systems
- Proficiency in visually diagramming architectures and explaining technical trade-offs
- Strong proficiency in PyTorch or TensorFlow
- Practical experience with MLOps tools such as MLflow, Kubeflow, SageMaker Pipelines, or Airflow
- Experience with the machine learning lifecycle, including feature stores, registries, deployment, monitoring, and retraining
Quantiphi Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Quantiphi and has not been reviewed or approved by Quantiphi.
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Wellbeing & Lifestyle Benefits — Wellbeing initiatives such as monthly meeting-free AMA-Zen Days, health check-ups, and wellness counseling are designed to reduce burnout and support day-to-day balance. Broader wellness programs reinforce both physical and mental health.
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Flexible Benefits — Remote/hybrid options with flexible working hours provide meaningful autonomy over where and when work gets done. Flexible leave constructs, including sabbaticals and special day leaves, add practical adaptability to the package.
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Parental & Family Support — Paid parental leave in the U.S., alongside maternity and childcare support, signals solid backing for families. These family-oriented policies integrate with a wider health and wellness focus.
Quantiphi Insights
What We Do
Quantiphi is an award-winning AI-first digital engineering company driven by the desire to solve transformational problems at the heart of business. Quantiphi solves the toughest and complex business problems by combining deep industry experience, disciplined cloud, and data-engineering practices, and cutting-edge artificial intelligence research to achieve quantifiable business impact at unprecedented speed.
