Mid - Forward Deployment Engineer - USA

About Cogniify

At Cogniify, we believe AI should move beyond pilots and prototypes into real, governed, enterprise-scale production.
Sharper Data. Smarter AI. Real Results.

We partner with Fortune 100 and global enterprises to advance AI from readiness through to production — with the governance, financial discipline, and operational rigor that large organizations require. Our work is anchored in the 4S Intelligence framework — Sharper Analytics, Smarter AI, Scalable Systems, and Secured Governance — and spans strategy, engineering, and AI delivered as end-to-end ecosystems, not siloed projects.

We don't sell a one-size-fits-all platform — every engagement is a custom-built solution designed around a client's specific problem, data, and constraints. Our philosophy is simple: clarity, trust, and measurable outcomes.

We're seeking a Forward Deployed Engineer (Principal / Architect Level) to independently own AI transformation engagements with our enterprise clients — end to end, from executive discovery through hands-on solution design, build, and delivery.

Unlike a traditional delivery role, this position sits directly in front of the client. You will lead technical and business discovery conversations with CXOs and senior leadership, diagnose their real underlying problem (not just the one they lead with), identify the highest-value AI opportunity in their environment, and personally design and build a working Proof of Acceleration (PoA) — a custom solution built specifically for that client, typically within a 2–3 week cycle.

You will then present and defend that solution to client and investor-level stakeholders, and drive successful PoAs into full-scale Cogniify execution engagements.

This role is built for a senior architect who is as strong a problem solver and business thinker as they are an engineer: credible enough to earn trust with a CTO in the first meeting, sharp enough to see the real business problem behind a vague ask, and hands-on enough to still be writing the code that proves the point.

• Own client engagements end to end — from discovery and opportunity framing through architecture, hands-on build, demonstration, and handover to execution teams.
• Lead discovery and working sessions directly with client CXOs, engineering leaders, and investor/board-level stakeholders to understand their business, uncover the real problem behind the stated ask, and frame the case for AI transformation.
• Assess a client's existing systems, data landscape, AI maturity, and engineering constraints, and translate ambiguous, often messy business problems into clearly scoped technical solutions.
• Architect and personally build production-credible, client-specific Proofs of Acceleration — including data pipelines, integrations, and AI/LLM-powered applications — within tight (2–3 week) delivery windows.
• Design and implement AI/ML-powered features such as RAG pipelines, LLM-based workflows, document processing, search/retrieval systems, and agentic or conversational AI, tailored to each client's environment.
• Integrate diverse enterprise data sources and systems: relational databases, data warehouses, REST/GraphQL APIs, event streams, SaaS platforms (Salesforce, Workday, SAP, etc.), and unstructured data.
• Deploy solutions on client cloud infrastructure (AWS, Azure, or GCP), ensuring security, scalability, and operational readiness even at prototype stage.
• Build the business narrative behind each PoA — quantifying ROI and connecting the technical solution to outcomes executives care about (cost, speed, revenue, risk).
• Present and defend solution designs live in front of technical and executive audiences, handling scrutiny and pushback in real time.
• Own stakeholder management across…