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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. 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 up the client's own team alongside the system pair with their people, explain the reasoning behind each decision, and leave them able to run and improve what you built after you've moved 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. 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.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.
