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!

Senior Machine Learning Engineer
Experience Level: 3-6 years
Job Summary
As a Senior Machine Learning Engineer at Quantiphi, you will build, deploy, and maintain production-grade AI/ML solutions for Fortune 500 enterprise clients on Google Cloud Platform. You'll engineer intelligent systems spanning generative AI, agentic workflows, traditional machine learning, and computer vision. This is a hands-on role for builders who thrive on shipping production systems that solve real business problems at enterprise scale.

Responsibilities
Generative AI & Agentic Systems - - - -
Design and implement generative AI applications including RAG systems, agentic workflows, and multi-agent orchestration for complex business problems.

Build agentic systems combining memory, planning, and dynamic reasoning for multi-step problem-solving across enterprise datasets
Develop multi-agent architectures using modern orchestration frameworks with reliable communication and observability
Implement prompt engineering, context optimization, and evaluation frameworks for GenAI applications

MLOps & Production Engineering - - -
Own the complete ML lifecycle: CI/CD pipelines, automated testing, model versioning, validation gates, and progressive deployment
Build production APIs and microservices with authentication, error handling, and monitoring; design data pipelines and integrations
Monitor production ML systems, track model drift, maintain system reliability and implement A/B testing frameworks Knowledge Solutions
Architect knowledge graph and semantic search solutions enabling entity resolution, relationship discovery, and intelligent retrieval
Design hybrid retrieval combining vector embeddings with keyword search

Client Collaboration - -
Present technical solutions to clients, translating engineering decisions into business outcomes
Collaborate with architects, data engineers, and business analysts on integrated solutions

Required Qualifications - - - - -
Bachelor's degree in Computer Science, Engineering, Mathematics, or related field (or equivalent demonstrated experience)
3-6 years of hands-on ML engineering with demonstrated expertise across multiple domains (GenAI)
Expert-level Python proficiency with strong software engineering fundamentals: API design, testing, containerization Proven track record shipping production ML systems in cloud environments with GCP (Vertex AI, BigQuery, Cloud Run) or equivalent

Experience building GenAI, traditional ML, and computer vision applications; MLOps practices; retrieval-augmented generation