Associate Director, AI/ML Engineering

Primary Responsibilities Design, build, and deploy agentic AI workflows to automate complex business processes using multi‑agent orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI). Architect and implement MCP servers to expose enterprise tools, APIs, and data sources as standardized capabilities for AI agents. Integrate multi‑agent systems with enterprise databases, internal APIs, and MCP servers for grounded, context‑aware, action‑oriented AI solutions. Partner cross‑functionally to define data contracts, lineage standards, and quality thresholds for AI/ML use cases. Design agentic memory systems (short‑term, long‑term, episodic) and planning/reasoning loops for reliable autonomous execution. Evaluate agentic performance (accuracy, reliability, latency, cost, safety) using benchmarks and red‑teaming. Build guardrail frameworks (input/output filtering, moderation, policy enforcement, hallucination detection) to ensure safety and compliance. Develop RAG pipelines (chunking, embeddings, vector stores, retrieval optimization). Apply prompt engineering, few‑shot learning, and fine‑tuning for domain pharma use cases. Design and deploy traditional ML models (classification, regression, clustering, time‑series, survival analysis). Build and maintain end‑to‑end ML pipelines with LLM Ops/ML Ops standards (registry, evals, prompt/version control, observability, rollback). Other responsibilities as assigned. Qualifications and Skills Master’s or PhD in Machine Learning, Computer Science, Data Science, Information Systems, or a related quantitative field. 7+ years AI/ML engineering, including 3+ years hands‑on Generative AI/agentic AI systems. Expertise in multi‑agent frameworks (LangGraph, AutoGen, CrewAI, Semantic Kernel, or similar). Experience building MCP servers and integrating AI with enterprise data/tools. Strong RAG experience (embeddings, vector databases). Proficiency in Python; frameworks such as PyTorch, TensorFlow, scikit‑learn, Hugging Face. Experience with ML Ops/LLM Ops (model lifecycle, evaluation, deployment). Ability to travel domestically/internationally. Real‑world data (RWD), claims, EHR, clinical study, translational/biological data is a plus. Benefits and Compensation Discretionary bonus and equity awards; salary range: $172,000–$215,000 USD. Medical, dental, vision; employer‑paid life, disability, business travel, and EAP. 401(k) match; employee stock purchase plan. 15+ vacation days; paid holidays; 10 days sick time; paid parental leave. Tuition assistance. #J-18808-Ljbffr Scorpion Therapeutics