Applied Scientist, GenAI & ML Systems

Company overview:
Trace Link is the world's largest Agentic Business Network, enabling life sciences and healthcare companies to build and manage a scalable digital workforce of governed, no-code AI agents that execute and coordinate mission-critical supply chain operations alongside human teams. Powered by the Integrate-Once™ OPUS platform, Trace Link links more than 300,000 network participants, enabling multi-enterprise processes at global scale. Founded in 2009 with the simple mission of protecting patients, today Tracelink has 5 global offices, over 800 employees and more than 1700 customers in over 60 countries around the world.

Our expanding product suite continues to protect patients and now also enhances multi-enterprise collaboration through innovative new applications such as MINT. Tracelink is recognized as an industry leader by Gartner and IDC, and for having a great company culture by Comparably.

Location:

Wilmington, MA (US) - Fulltime Onsite

We are hiring an Applied Scientist to lead the design and deployment of production‑grade GenAI and ML systems with a strong emphasis on being hands‑on. You will personally build, iterate, and ship systems focused on LLM/SLM optimization for agentic, multi‑agent architectures in cloud environments. This role is ideal for someone with deep expertise in one or more areas of LLM/SLM optimization for agent-based systems, and hands‑on experience in designing, implementing, and operating large‑scale multi‑agent systems in the cloud.

• Hands‑on ownership of building and shipping multi‑agent systems (planner/executor, tool‑using agents, supervisor patterns, routing, role‑based agents) from prototype to production.
• Write production‑quality code for agent orchestration, tool integration, memory/state design, and context management.
• Lead context engineering strategies for multi‑agent coordination: prompt design, state persistence, agent handoffs, grounding, constraints, and safety controls.
• Hands‑on fine‑tune and deploy SLM models for production usage: dataset creation, training workflows, evaluation, and inference serving.
• Build Advanced RAG pipelines end‑to‑end, including semantic search, embeddings, hybrid retrieval, and cross‑encoder reranking.
• Implement evaluation frameworks for multi‑agent systems covering quality, latency, cost, robustness, and failure mode detection.
• Collaborate with platform and product engineering to ensure solutions are cloud‑native, secure, observable, and scalable (monitoring, logging, CI/CD).
• Optimize for cost and latency via model routing, caching, compression strategies, and inference efficiency improvements.
• Mentor peers through code reviews, architecture sessions, and hands‑on technical leadership.

• Context engineering for complex multi‑agent systems (prompt orchestration, tool calling, memory/state design, routing, constraint handling)
• Fine‑tuning of SLMs and delivering them to production (training strategies, validation, deployment, monitoring, rollback readiness)
• Experience with Advanced RAG, semantic search, embeddings, and cross‑encoders (retrieval tuning, chunking strategies, query rewriting/planning, reranking)
• Ability to translate ambiguous requirements into concrete architectures, metrics, and deliverables
• Hands‑on inference optimization experience: quantization, distillation, batching, caching, model routing, speculative decoding
• Experience building retrieval systems at scale using vector DBs and search stacks
• Comfort working across the full lifecycle: research - prototype - A/B test - production hardening

• Familiarity with enterprise constraints: privacy, security, data governance, permissions, auditability
• Experience designing and running GenAI observability: traces, prompt/versioning, tool call logging, feedback loops
• Strong ability to implement production‑quality systems in Python (and/or adjacent backend languages)
• Proven experience deploying GenAI/ML systems in cloud environments (AWS/Azure/GCP)
• Experience with scalable inference and service operations: containers, APIs, observability, reliability…