4 days ago
Remote, United StatesSenior
Responsibilities
- Embed with business units and stakeholder teams to understand workflows, data, and pain points.
- Scope and ship Generative AI and Agentic AI proofs of concept, MVPs, and production features within days or weeks.
- Build RAG pipelines, apply prompt engineering, and create evaluation frameworks to validate solution accuracy.
- Design API-first integrations connecting AI capabilities to enterprise systems and workflows.
- Translate ambiguous business needs into technical specifications and explain technical tradeoffs to executives and business stakeholders.
- Lead stakeholder workshops, present progress, and proactively identify risks.
- Support stabilization, adoption, and issue resolution after deployment.
- Contribute reusable components, improvements, and lessons learned to the shared AI platform and toolkit.
- Apply MLOps and LLMOps practices, including monitoring and model or agent lifecycle management, to maintain reliable production solutions.
Requirements
- Bachelor’s or master’s degree in Computer Science, AI, Data Science, or a related field.
- 8+ years of overall software engineering or IT experience.
- 3+ years of hands-on experience building Generative AI or Agentic AI systems.
- Strong expertise in Python and modern AI development frameworks.
- Practical experience with LLMs and multiagent orchestration frameworks such as LangGraph, AutoGen, or Semantic Kernel.
- Deep understanding of RAG architectures, vector databases, and embedding models.
- Experience with at least one major cloud platform—Azure, AWS, or GCP—including native AI services.
- Experience with MLOps or LLMOps, CI/CD, containerization, observability, and evaluation frameworks.
- Ability to embed with business or customer teams, including onsite engagement as needed.
- Strong communication skills and the ability to lead workshops and present to executives and engineers.
- Ability to own solutions end-to-end in ambiguous, fast-moving environments.
- Preferred experience in forward deployment, consulting, or solutions architecture.
- Familiarity with Model Context Protocol and emerging AI agent ecosystems.
- Experience building evaluations with Ragas, LangSmith, or custom harnesses.
- Healthcare, finance, or government domain fluency and experience with healthcare distribution, specialty pharma data, or EMR/EHR systems is preferred.
- Experience building reusable frameworks or components across multiple engagements is preferred.
Benefits
- Medical, dental, and vision care.
- Backup dependent care, adoption assistance, infertility coverage, family-building support, behavioral health solutions, paid parental leave, and paid caregiver leave may be available.
- Training programs, professional development resources, mentorship programs, employee resource groups, and volunteer activities.
- Full-time position with onsite engagement as needed.
Tech Stack
Categories
Forward Deployed
