Forward Deployed AI Solutions Engineer

Excerpt from the original posting

About the role 

The Forward Deployed Solutions Engineer will work directly within a business domain (e.g., Commercial, Clinical Operations, Lab Operations, Sales & Marketing, Customer Experience etc.). In your role, you’ll find opportunities for enhancing efficiency and productivity by looking for workflows which can be executed 10–100x faster or more often than a human team could using AI agents, integrations and other patterns. You will build, deploy, and run them in production.

You report into the central AI & Automation team, partner directly with domain leadership on priorities, and bring patterns back so the whole company compounds.

Find the leverage in your domain 

- Map the workflows in your domain — the ones running today, and the ones that don’t exist yet because they weren’t feasible without agents or automation tools.

- Identify the step-change opportunities: where AI, ML, or automation unlock throughput, coverage, or speed.

- Build the business case, quantify projected impact, and align with domain leadership on priorities. 

Design the future-state workflow 

- Map structured and unstructured data flows across the systems involved (CRM, ERP, ticketing, document stores, internal tools, external SaaS).

- Define the target workflow: what the agent does, what the human does, and where they hand off.

- Figure out what context the agent or model needs to do the work well — and how to get it there reliably (retrieval, grounding, tool access, memory).

- Design human-in-the-loop checkpoints so review adds value without becoming the bottleneck.

Build and connect the systems 

- Stand up agents and automation pipelines using the organization’s approved AI platforms and frameworks.

- Connect agents to business systems — via MCP servers, APIs, webhooks, CLIs, and skills — within the guardrails set by IT and security.

- Configure tools, prompts, context, and retrieval pipelines so agents perform reliably on real work, not just in demos.

- Handle integration gnarliness: auth, schema drift, rate limits, data quality, and the messy last-mile of enterprise systems.

- Enable access and training for business to run the workflows

Run agents and automation pipelines in production 

- Own agent performance end-to-end. Track the KPIs that matter — throughput, quality, cost, human intervention rate, cycle time, adoption.

- Build and manage evals. Re-run them on any material model, data, or workflow change before it ships.

- Triage failures, tune prom…

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