Forward Deployed AI Solutions Engineer
Heavy focus on building and running AI agents, prompt engineering, and agentic platforms (LangChain, CrewAI, AWS Bedrock); hands-on AI automation work with production evals and observability.
About the Role
Forward Deployed AI Solutions Engineer who partners with business domains to design, build, deploy, and run AI agents and automation pipelines in production, driving measurable improvements in throughput, quality, and speed. The role blends hands-on engineering (APIs, webhooks, SQL, Python, cloud) with product and stakeholder-facing responsibilities to embed agentic workflows into domain operations.
Job Description
Role
The Forward Deployed AI Solutions Engineer partners directly with a business domain (e.g., Commercial, Clinical Operations, Lab Operations, Sales & Marketing, Customer Experience) to find high-leverage opportunities for AI agents and automation. You will design future-state workflows, build and integrate agents into existing systems, and run those agents and pipelines in production with observability and evaluation in place.
Key Responsibilities
- Map domain workflows and identify step-change opportunities where AI/automation can increase throughput, coverage, or speed.
- Build business cases, quantify impact, and align priorities with domain leadership.
- Design target workflows, defining agent vs. human responsibilities and handoffs, including human-in-the-loop checkpoints.
- Map structured and unstructured data flows across systems (CRM, ERP, ticketing, document stores, internal tools, external SaaS).
- Stand up agents and automation pipelines using approved AI platforms and frameworks; connect agents to business systems via servers, APIs, webhooks, CLIs, and skills.
- Configure prompts, context, retrieval pipelines, and tool access; handle integration challenges (auth, schema drift, rate limits, data quality).
- Operate agents in production: own performance KPIs (throughput, quality, cost, human intervention rate, cycle time, adoption); run and manage evals; triage failures and iterate.
- Instrument observability: tracing, structured logs, dashboards.
Requirements
- Hands-on technical fluency with CLIs, APIs, webhooks, SQL, and Python scripting.
- Working knowledge of LLM and agent behavior, including prompting, context, tool use, retrieval-augmented generation (RAG), MCP, evals, and common failure modes.
- Comfortable working with a cloud platform and operating with elevated access under least-privilege and audit constraints.
- Strong technical and process judgment, outcome and KPI-driven, able to communicate effectively with both business and technical stakeholders.
Nice to have
- Prior experience delivering measurable outcomes while embedded with business teams.
- Hands-on experience with enterprise agentic platforms or model APIs (examples called out: CrewAI, LangChain, AWS Bedrock, Claude, Codex).
- Background in product management, solutions engineering, consulting, forward-deployed engineering, or technical operations.
- Experience in regulated environments (HIPAA, SOC 2, GxP, SOX).
Success in Year One
- Ship three or more production workflows that move a business KPI with agent evals and observability in place.
- Be engaged by domain leadership in planning early and contribute reusable assets used by other engineers.
- Enable non-builders to become builders using the artifacts you create.
Compensation & Location
- Base salary ranges provided: Standard cost of living areas $105,700 - $132,100; Higher cost of living areas $116,200 - $145,300; Lower cost of living areas $95,100 - $118,900. Additional components such as bonus and equity are included.
- Role listed as Remote USA.