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
Traditional ML & Computer Vision
- Design and implement ML pipelines for forecasting, recommendations, classification, and regression problems at scale
- Build production computer vision systems for document understanding, image analysis, and visual enterprise applications
- Develop feature engineering strategies and statistical models; optimize models for production using hyperparameter tuning and performance benchmarking
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, traditional ML, computer vision)
- 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
Preferred Qualifications
- Google Cloud Professional Machine Learning Engineer certification
- Knowledge graph and semantic search implementations; regulated industry experience (Healthcare, Financial Services)
- Published technical content or open-source contributions
