Senior AI-ML Engineer

Senior AI/ML Engineer Advanced Analytics & AI Products Building across supply chain, life sciences, financial services, defense and public sector

• Fundamentals: strong linear algebra, probability and statistics — vector spaces, distance and similarity metrics, distributions, calibration, hypothesis testing. Non-negotiable.
• Classical AI/ML: gradient boosting, random forests, SVM, clustering, anomaly detection (Isolation Forest, LOF, one-class SVM), survival analysis, time-series forecasting, optimization, feature engineering and model evaluation.
• Deep learning and transformers: transformer architecture and self-attention, embeddings, representation learning, sequence and graph models; PyTorch or TensorFlow.
• Applied GenAI: hands-on with Claude, OpenAI and Gemini APIs — embeddings, prompt and context engineering, structured output, tool use, agentic workflows, RAG and semantic retrieval, evaluation of model output; plus AI-assisted development (Claude Code, Copilot, Cursor) to ship faster.
• Data representation: embedding heterogeneous records (text, categorical, sequence, relational) into high-dimensional spaces; similarity and cohort transfer, drift and change detection, graph-based propagation across connected entities.
• Engineering: Python (primary) and Java; streaming and batch pipelines, entity resolution, PostgreSQL + pgvector, FastAPI, Docker, cloud deployment. Preferred: regulated-environment delivery (federal IL4/IL5, RMF/STIG, HIPAA, or financial compliance) and domain data at scale.