Forward Deployed Engineer

Job Summary
We are seeking an experienced Forward Deployed Engineer (FDE) to work directly with strategic enterprise customers to architect, build, and deploy high-impact AI solutions. This role combines software engineering, AI/ML, data engineering, customer engagement, and end-to-end solution ownership. The FDE will serve as the technical bridge between AI platform capabilities and customer business challenges, owning the solution lifecycle from problem discovery and rapid prototyping through production deployment and continuous optimization. Travel up to 25% may be required for on-site customer engagements.


Key Responsibilities
• Diagnose critical business challenges, map data landscapes, and co-design AI solutions with customers.
• Lead end-to-end solution design and delivery of agentic AI workflows, RAG pipelines, knowledge graphs, and real-time decision-making applications.
• Drive rapid prototyping and proof-of-concept (POC) development to demonstrate business value within days to weeks.
• Serve as the primary technical owner across the full project lifecycle, including scoping, architecture, development, deployment, and post-launch optimization.
• Architect production-grade Enterprise AI applications on Partner Foundry Solutions or Rackspace Private Cloud and GPU infrastructure.
• Integrate AI solutions with enterprise systems including ERP, CRM, data warehouses, data lakes, and streaming platforms.
• Build scalable data pipelines across structured and unstructured data using ETL/ELT, vector databases, and knowledge base frameworks.
• Develop and fine-tune LLM/SLM solutions and implement RAG architectures using frameworks such as LlamaIndex and Haystack.
• Orchestrate multi-agent workflows using frameworks such as LangChain, LangGraph, and CrewAI.
• Develop full-stack solutions using Python, Node.js/Go, React/Vue, SQL/NoSQL databases, Docker, and Kubernetes.
• Implement CI/CD pipelines and support GPU cluster management and cloud-native deployment patterns.
• Champion observability, monitoring, and telemetry to ensure trustworthy, auditable, and versioned AI agents in production.
• Identify expansion opportunities by collaborating with sales and customer success teams to uncover high-value AI use cases across business domains.
• Provide structured field insights to Platform Engineering and Product teams regarding feature gaps, emerging customer needs, and usability improvements.
• Build reusable intellectual property through reference architectures, accelerators, frameworks, and technical best practices.
• Mentor engineers and customer teams through knowledge transfer and development of internal AI capabilities.


Required Qualifications
• Palantir certification is required.
• 6+ years of experience in software engineering, data engineering, or AI/ML delivery, including at least 4+ years in customer-facing or field roles.
• Proven experience building and deploying AI/ML applications in production at enterprise scale.
• Strong full-stack proficiency with Python, Node.js/Go, React/Vue, and SQL/NoSQL databases.
• Hands-on experience with LLMs, prompt engineering, vector databases, data pipelines, application dashboards, RAG pipelines, and agent orchestration frameworks.
• Strong DevOps experience with Docker, Kubernetes, CI/CD, GPU infrastructure, and cloud-native deployment patterns.
• Experience integrating heterogeneous enterprise systems, including ERP, data warehouses, data lakes, and streaming architectures.
• Ability to translate ambiguous customer requirements into actionable engineering plans under tight timelines.
• Excellent communication skills with the ability to conduct C-suite presentations, technical workshops, and cross-functional collaboration.
• Experience with Palantir Foundry, AIP, ontology modeling, Uniphore BAIC, or similar Enterprise AI development platforms.
• Knowledge of SLM fine-tuning, model distillation, RLHF, and AI evaluation frameworks.
• Experience building agentic AI solutions, including multi-agent systems, tool use, and autonomous workflow orchestration.
• Familiarity with GPU infrastructure, including NVIDIA H100/B200 and InfiniBand, and private cloud platforms such as OpenStack and VMware.
• Experience in technology consulting, AI startups, Forward Deployed Engineering, or Solutions Engineering roles.
• Experience integrating enterprise AI solutions across complex technical environments.


Preferred Qualifications
• Domain expertise in financial services, healthcare, supply chain, defense, energy, or manufacturing.
• Experience with knowledge graphs, semantic modeling, and ontology-driven data management.
• Experience with NVIDIA GPU infrastructure and private cloud environments.
• Experience developing reusable AI accelerators, reference architectures, and enterprise AI frameworks.