USAApplied Scientist, GenAI & ML Systems
2 нед. назад
USASeniorOnsite
llmmlcloud
Lead design and deployment of production-grade generative AI and ML systems with focus on LLM/SLM optimization for multi-agent architectures.
Обязанности
- 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.
Будет плюсом
- 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)
- 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.
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Другое
- 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, 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 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.
- is recognized as an industry leader by Gartner and IDC, and for having a great company culture by Comparably.
- 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