> Markdown version of [/jobs/ext/1685818-forward-deployed-engineer](https://www.wearedevelopers.com/jobs/ext/1685818-forward-deployed-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Forward-Deployed Engineer - **Company:** NTT DATA, Inc. - **Location:** Plano, TX, United States (Remote available) - **Experience:** Experienced - **Salary:** $158,962.0 - **Contract:** Permanent contract - **Skills:** JavaScript (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Application Integration Architecture, Microsoft Azure, Cloud Computing, Information Engineering, Python (Programming Language), Machine Learning, Cloud Services, Tensorflow, Software Engineering, Systems Integration, TypeScript, Workflow Management Systems, Google Cloud, Pytorch, ReactJS, Flask (Web Framework), Large Language Models, Snowflake, Fastapi, Machine Learning Operations, Virtual Agents, Databricks, Programming Languages - **Published:** July 17, 2026 - **Apply:** https://www.dice.com/job-detail/7470752f-e2bd-4b76-9a24-90ea42133a58 ## About the Role 12+ years of experience in software engineering, AI engineering, data engineering, ML engineering, solution engineering, technical consulting, or related hands-on technical roles. - 2+ years of experience building or deploying AI, ML, GenAI, LLM, RAG, agentic AI, data science, or automation solutions. - 2+ years of hands-on experience with programming languages and frameworks such as Python, JavaScript/TypeScript, FastAPI, Flask, React, LangChain, LlamaIndex, PyTorch, TensorFlow, or similar technologies. - 2+ years of experience integrating APIs, data sources, model providers, workflow tools, and cloud services into working demos or prototypes. - Bachelor's degree or equivalent work experience. Preferred Skills: - Experience with OpenAI, Anthropic, Mistral, Google/Gemini, Azure, AWS, Google Cloud Platform, Databricks, Snowflake, vector databases, model registries, and MLOps/LLMOps tooling. - Experience working directly with clients, sales teams, or product teams to rapidly prototype solutions under ambiguous conditions. - AI/ML, cloud, data engineering, or software engineering certifications. - Strong technical curiosity, client empathy, communication, bias for action, collaboration, and ability to explain complex technical concepts clearly. - Ability to travel as required for client and internal engagements. ## Description The Forward-Deployed Engineer is a client-facing builder within the AI GTM organization responsible for rapidly creating demos, prototypes, proofs of concept, and MVPs that make AI opportunities tangible for clients and account teams. This role combines hands-on engineering, AI fluency, product thinking, and field presence to translate client problems into working technical concepts using models, agents, data, workflows, integrations, and user experiences., Rapidly build demos, prototypes, proofs of concept, and MVPs that bring AI use cases to life for clients and account teams. - Translate client problems into working technical concepts using models, agents, data, workflows, APIs, integrations, and user experience patterns. - Support discovery workshops, executive demos, technical deep dives, proof-of-value sessions, and partner enablement activities. - Partner with Solution Architects to ensure prototypes align to scalable architecture, delivery paths, security expectations, and operational requirements. - Work with ecosystem partners to showcase differentiated capabilities across OpenAI, Anthropic, Mistral, Google/Gemini, cloud, data, and enterprise platforms. - Package prototypes into reusable assets, demo scripts, technical collateral, setup guides, reference architectures, and handoff materials. - Validate technical feasibility, data dependencies, integration complexity, user experience assumptions, and implementation risks early in the sales cycle. - Help accelerate opportunity conversion by making AI solutions concrete, credible, and client-specific. - Contribute lessons learned, reusable code, prompts, agent patterns, and implementation guidance back into the global FDE knowledge base. Success Measures: - Pursuit deal volume, measured as total TCV value of AI deals where the FDE supported. - Pursuit win rate for AI deals where the FDE supported. - Prototype-to-pipeline conversion, measured by demos, prototypes, or POCs that convert into qualified pipeline, funded POCs, or downstream implementation work. - Utilization, reuse of technical assets, demo quality, and stakeholder feedback. ## Related Videos - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [Intro to FastAPI](https://www.wearedevelopers.com/videos/462-intro-to-fastapi) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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