Forward AI Deployment Engineer (FDE)

Are you energized by walking into a client’s operation, finding the workflow that’s actually holding them back, and rebuilding it so AI handles the parts that don’t need a person? At Sigma Software, we don’t just add AI on top of existing processes  we redesign how the work gets done, with AI built into the operating logic from day one.

We’re looking for a Forward AI Deployment Engineer to own that redesign end to end: embedded with the client, hands-on with whatever automation the workflow actually needs, and accountable for turning a real operational problem into something running in production not a demo, not a pilot. Each engagement is built to leave something behind for the next one patterns, infrastructure, governance that already held up in production  so the second engagement doesn’t start from zero, and the tenth looks nothing like the first.

• Proven experience taking AI systems from prototype to production inside a real operating environment, not just in a lab or sandbox.

• You treat LLM/RAG architectures, agentic systems, cloud platforms, and data pipelines as tools for redesigning how work gets done, not as the deliverable — and you’re strong and current enough with all of them to actually use them that way.

• Comfort mapping decision logic that’s partly undocumented, and judgment about where automation should stop, and human accountability should start.

• Excellent communication and stakeholder management — able to advise a client directly and escalate without being asked.

• BFSI domain experience is a must-have: Proven commercial experience delivering solutions for Banking, Financial Services, or Insurance, including understanding business processes, identifying operational challenges, and implementing technology solutions tailored to the domain

• Embed directly with the client’s operational team to find the workflow that’s actually costing them time, quality, or money before recommending any tooling.

• Own the redesign end to end: rework the decision flow and handoffs around AI, then take the resulting system into production yourself.

• Translate a vague, cross-functional business problem into a concrete technical plan the client’s team can actually execute against.

• Prototype fast enough to show the client what’s possible, then build only the parts of that prototype the redesigned workflow actually needs.

• Where the redesign calls for it, build the agentic systems, RAG pipelines, or custom data pipelines that make it real never a deliverable in itself, always because the workflow needs it running in production.

• Manage stakeholder alignment across the client’s org, escalating early and requesting support before a blocker becomes a delay.

• Build the client’s team alongside the system, explain the reasoning behind each decision, and leave them able to run and improve what you built after you move to the next engagement.

• Build the feedback loop from day one: telemetry, evaluation pipelines, human-in-the-loop review — so the system keeps improving after you’ve moved on.