Company overview:
TraceLink 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, TraceLink 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
About the RoleWe 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.
Key ResponsibilitiesHands-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 practices
MS/PhD in CS/ML/NLP/Stats (or equivalent applied experience building production systems)
TraceLink is committed to providing competitive compensation and benefits to all employees. This is the estimated base salary range for this role and should serve only as a guide. Final compensation offered may vary based on a variety of factors including but not limited to experience level, fit for the role, skills, domain knowledge, internal equity, budget, and location.
Please see the Tracelink Privacy Policy for more information on how Tracelink processes your personal information during the recruitment process and, if applicable based on your location, how you can exercise your privacy rights. If you have questions about this privacy notice or need to contact us in connection with your personal data, including any requests to exercise your legal rights referred to at the end of this notice, please contact [email protected].
Skills Required
- Hands-on building and shipping multi-agent systems (planner/executor, tool-using agents, supervisor patterns, routing, role-based agents)
- 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)
- Build Advanced RAG pipelines end-to-end including semantic search, embeddings, hybrid retrieval, and cross-encoder reranking
- Hands-on inference optimization: quantization, distillation, batching, caching, model routing, speculative decoding
- Experience building retrieval systems at scale using vector DBs and search stacks
- Write production-quality code for agent orchestration, tool integration, memory/state design, and context management
- Ability to translate ambiguous requirements into concrete architectures, metrics, and deliverables
- 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 practices
- MS/PhD in CS/ML/NLP/Stats or equivalent applied experience
What We Do
TraceLink is the only network creation platform company that builds integrated business ecosystems with multienterprise applications - the true foundation for digitalization - delivering customer-centric agility and resiliency for end-to-end supply networks and leveraging the collective intelligence of entire industries. Delivering end-to-end supply chain solutions, TraceLink's Opus Platform enables speed of innovation and implementation with an open partner model for no-code and low-code development of solutions and applications. At TraceLink, we blend decades of knowledge in SaaS technology and supply chain business processes with a clear vision for advancing manufacturing industries through disruptive, unconventional software solutions. With headquarters in Massachusetts, TraceLink has six global offices through North America, South America, Europe, and Asia.
