Company overview:

TraceLinks software solutions and Opus Platform help the pharmaceutical industry digitize their supply chain and enable greater compliance visibility and decision making. It reduces disruption to the supply of medicines to patients who need them anywhere in the world.

Founded in 2009 with the simple mission of protecting patients today Tracelink has 8 offices over 800 employees and more than 1300 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.

Staff / Senior Applied Scientist GenAI & ML Systems

Location: Wilmington MA (US) - Fulltime Onsite

About the Role

We are hiring a Staff / Senior 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.

Key Responsibilities

• 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.

Required Knowledge & Experience

• 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

Preferred Qualifications

• 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 practices

• MS/PhD in CS/ML/NLP/Stats (or equivalent applied experience building production systems)

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