VP, Forward Deployed AI Engineering

BGB Group • New York City, NY

5d

About The Position

BGB Group is a healthcare communications agency that offers a wide range of services, including traditional pharmaceutical advertising, promotional medical education, payer marketing, and consulting services. Known for excellence and professionalism, we’re hired as strategic and creative partners by our biopharmaceutical clients to drive category/brand awareness and growth. VP, Forward Deployed AI Engineering (Hands-On AI Transformation) Position Overview We’re hiring a hands-on Forward Deployed AI Engineering leader who can deploy AI automation and agentic systems in real workflows, quickly and reliably. You’ll work across internal teams and client-facing programs to prototype, integrate, and operationalize AI capabilities (LLM apps, agents, orchestration, eval/observability, and workflow automation). This is not a “strategy-only” role. We need a builder who can ship , integrate , and operate systems end-to-end.

Requirements

• Hands-on engineering background (you build systems yourself): strong Python plus one backend stack (TypeScript/Node, .NET, or similar).
• Experience deploying LLM applications in real orgs: prompts, tools/function calling, retrieval, evaluation, and iteration loops.
• Experience with LLM orchestration and building multi-step workflows (agents, DAGs, tool use, human approvals).
• Ability to design and implement APIs and integrations across enterprise systems (auth, permissioning, data flow, reliability).
• Practical experience with LLM observability and evaluation (quality metrics, regression tests, traces, cost controls).
• Comfortable in client-facing or stakeholder-heavy environments: can explain what’s possible and what’s not clearly.

Nice To Haves

• Multi-agent systems in production, or agent frameworks at scale.
• Enterprise automation/workflow experience (process automation, knowledge base integration, CRM/ERP or content systems).
• Experience in regulated industries (healthcare/life sciences) including audit trails and compliance constraints.
• Azure ecosystem experience (Azure AI Foundry or Azure-hosted LLM stacks), plus identity/permissions patterns.
• Contributions to internal developer platforms, reusable tooling, or open-source.
• You move fast but don’t break trust: pragmatic engineering, strong prioritization, and reliable delivery.
• You like ambiguity and can turn it into a working system.
• You’re energized by bridging demo and deployment and owning the full lifecycle.

Responsibilities

• Forward Deployed Delivery (Prototype to Production) Rapidly build client-relevant demos and proofs of concept that reflect real BGB workflows (content generation, modular build, review/MLR support, routing, measurement, etc.). Convert successful prototypes into production-ready implementations with security, access controls, auditability, and documentation. Partner with delivery teams to embed solutions into day-to-day operations, not slideware.
• Agentic AI and Workflow Automation Implementation Design and deploy agentic workflows (multi-step, tool-using agents; retrieval and reasoning; human-in-the-loop approvals). Stand up automation pipelines for repeatable tasks (brief-to-output flows, content QA checks, tagging/classification, summarization, knowledge retrieval). Build reusable components: templates, agent patterns, evaluation harnesses, and integration adapters.
• Systems Integration and APIs (Make it all work together) Write and maintain APIs/services that connect LLM systems to enterprise tools (content repositories, project systems, data stores, analytics, identity/access). Implement RAG and knowledge services (indexing, retrieval, permission-aware access, citations, provenance). Orchestrate data flows across systems with strong logging, error handling, and versioning.
• LLM Ops: Observability, Evaluation, Reliability Implement LLM observability (traces, cost, latency, quality) and evaluation (offline and online, regression testing, guardrails). Own the operational reality: monitoring, incident response patterns, rollbacks, prompt/model version control. Establish practical standards for reliability and safety in regulated environments.
• Enablement (Technical + Practical) Translate needs into technical designs and explain tradeoffs clearly to non-engineers. Create lightweight playbooks and internal documentation so teams can adopt and extend what you build. Support proposal technical sections and scoping with grounded, buildable architectures.

What This Job Offers

• Job Type: Full-time
• Career Level: Executive
• Education Level: No Education Listed