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
What Sets This Role Apart
Work on transformational AI projects for Fortune 500 companies across multiple AI/ML domains.
Build production systems impacting millions while developing expertise spanning GenAI,
traditional ML, and computer vision. Collaborate with leading practitioners and Google Cloud
teams on emerging technologies while growing into architecture and leadership roles.