Mid - Forward Deployment Engineer - USA

Cogniify•Mountain View, CA

5d•$165,000 - $175,000•Remote

About The Position

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.

Requirements

3-6+ years of overall technology experience, including significant time in architect-level or technical lead roles on complex, enterprise-grade systems.
Strong business acumen — genuinely curious about how a client's business works, able to get past the surface-level ask to the real underlying problem before jumping to a solution.
Exceptional problem-solving ability — comfortable with ambiguity, able to structure an open-ended or poorly defined problem and independently arrive at a workable, well-reasoned solution.
Demonstrated experience owning solutions end-to-end — from client conversation to architecture to hands-on build to executive presentation — not just one slice of the lifecycle.
Strong, current hands-on proficiency in Python and SQL, with a track record of building production-quality data pipelines, APIs, and applications personally (not just directing others).
Deep experience with modern data platforms (Snowflake, Databricks, BigQuery, or Redshift) and orchestration tools (dbt, Airflow, Dagster, or Prefect).
Strong grounding in AI/GenAI application patterns: LLM integration, RAG pipelines, embeddings, vector databases, and agentic workflows using frameworks such as LangChain or LlamaIndex.
Working knowledge of at least one major cloud platform (AWS, Azure, or GCP), including compute, storage, networking, and managed AI/data services.
Proven ability to engage directly with C-level and senior executive stakeholders — framing ambiguous problems, facilitating prioritization discussions, and presenting technical solutions in business terms.
Strong stakeholder management skills — able to manage competing priorities across client, investor, and internal audiences, and build trust quickly in new environments.
Prior experience in client-facing consulting, pre-sales engineering, solutions architecture, or a founding/lead engineering role at a fast-moving company.
Comfort with rapid context-switching across unfamiliar codebases, domains, and technology stacks under tight delivery timelines.
Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field (or equivalent experience).

Nice To Haves

Experience with MCP (Model Context Protocol) or similar tool-layer/agent-integration patterns.
Experience building client-facing applications, dashboards, or internal tools using React, Next.js, or low-code platforms (Retool, Appsmith).
Experience with containerization and infrastructure-as-code (Docker, Kubernetes, Terraform) for deployment automation.
Familiarity with data quality and observability tooling (Great Expectations, Monte Carlo, dbt tests, or Soda).
Prior experience working with private equity portfolio companies, or in transformation programs tied to investor/board reporting cycles.
Domain experience in financial services, healthcare, SaaS, or enterprise operations.

Responsibilities

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 concurrent relationships — technical teams, business sponsors, and investor-side stakeholders — balancing competing priorities with confidence.
Partner with account and engagement leadership to convert successful PoAs into full-scale Cogniify execution engagements, and produce handover documentation for delivery teams.
Mentor junior FDEs and contribute reusable frameworks, accelerators, and playbooks that speed up future engagements.
Operate as a mobile, high-trust resource — moving from one client engagement to the next as PoAs conclude.