AI Solutions Engineer - Life Sciences

This role involves leading the design and delivery of AI solutions for Life Sciences clients, focusing on agentic systems and LLM-enabled applications. It requires experience in cloud data architectures, RAG, embeddings, vector databases, orchestration frameworks, tool-use, and LLMOps practices like evaluation, monitoring, prompt management, and guardrails. The role also involves leading engagements, managing stakeholder relationships, and overseeing team deliverables.

What you'd actually do

  1. Lead small engagements or key workstreams within large, complex data and analytics transformations for Life Sciences clients
  2. Design and implement cloud and hybrid data architectures, migration approaches, and technology solutions across data and analytics platforms
  3. Manage day-to-day stakeholder relationships and align technical delivery to business and program objectives
  4. Oversee quality of team deliverables and recommendations while contributing to hands-on solution delivery across distributed teams
  5. Support practice growth through proposal development, thought leadership, and coaching junior practitioners

Skills

Required

  • AI/GenAI solution architecture
  • agentic systems
  • LLM-enabled applications
  • RAG
  • embeddings
  • vector databases
  • orchestration frameworks
  • tool-use architectures
  • LLMOps
  • evaluation
  • monitoring
  • prompt lifecycle management
  • guardrails
  • Cloud and hybrid data architectures
  • Data migration approaches
  • Technology solutions across data and analytics platforms
  • Stakeholder management
  • Team leadership
  • Solution delivery

Nice to have

  • Life Science R&D AI Solutions experience
  • Consulting experience
  • Client-facing delivery roles
  • Creating critical collaterals for client workshops
  • Presenting technical content

What the JD emphasized

  • 6+ years hands-on experience delivering AI solutions for Life Sciences
  • 1+ years hands-on experience with AI/GenAI solution architecture, especially agentic systems and LLM-enabled applications
  • 1+ years experience with RAG, embeddings, vector databases, orchestration frameworks, memory/state patterns, and tool-use architectures
  • 1+ years experience building or governing LLMOps practices such as evaluation, monitoring, prompt lifecycle management, and guardrails
  • 2+ years experience leading, managing and delivering complex AI Solutions engagements

Other signals

  • AI/GenAI solution architecture
  • agentic systems
  • LLM-enabled applications
  • RAG
  • embeddings
  • vector databases
  • orchestration frameworks
  • tool-use architectures
  • LLMOps
  • evaluation
  • monitoring
  • prompt lifecycle management
  • guardrails