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