Senior Forward Deployed Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Forward Deployed Engineer based in United States.

This role places you directly alongside strategic enterprise customers to solve complex business challenges through advanced AI engineering.

You will own solutions end-to-end, from discovery and rapid prototyping through production deployment and continuous optimization.

The position blends hands-on software, data, and AI engineering with customer engagement, technical strategy, and business impact.

You’ll design production-grade agentic AI, RAG, knowledge graph, and real-time decision-making solutions across sophisticated enterprise environments.

Working closely with customer teams, sales, product, and platform engineering, you’ll turn ambiguous requirements into scalable technical solutions.

You’ll also create reusable architectures and accelerators while mentoring engineers and helping organizations build their AI capabilities.

This is a remote US opportunity with up to 25% travel and the autonomy of a startup-style environment backed by enterprise-scale resources.

Accountabilities

• Diagnose complex customer business challenges, assess data landscapes, and collaboratively define high-value AI opportunities and solution approaches.
• Lead the end-to-end design and delivery of agentic AI workflows, RAG pipelines, knowledge graphs, and real-time decision-making applications.
• Develop rapid prototypes and proof-of-concepts that demonstrate measurable business value within days or weeks.
• Serve as the primary technical owner throughout the solution lifecycle, including discovery, scoping, architecture, development, deployment, and post-launch optimization.
• Architect and deploy production-grade enterprise AI applications across private cloud, AI platforms, and GPU infrastructure, integrating with ERP, CRM, data warehouses, data lakes, and other enterprise systems.
• Build scalable data pipelines supporting structured and unstructured data using ETL/ELT processes, vector databases, and knowledge-base frameworks.
• Develop and fine-tune LLM and SLM solutions, implementing RAG architectures and multi-agent workflows using modern AI orchestration frameworks.
• Apply full-stack and DevOps expertise across application development, containerization, Kubernetes, CI/CD, cloud-native deployment, and GPU infrastructure.
• Establish observability, monitoring, telemetry, versioning, and auditability practices to support trustworthy AI applications in production.
• Partner with sales and customer success teams to identify additional high-value use cases and opportunities for solution expansion.
• Translate field experience into structured feedback for product and platform engineering teams, highlighting feature gaps, emerging customer needs, and usability improvements.
• Build reusable intellectual property, including reference architectures, accelerators, frameworks, and technical standards that improve future engagements.
• Mentor engineers and customer teams through knowledge transfer, technical guidance, and development of internal AI capabilities.

Requirements

• Palantir certification is required.
• 10+ years of experience in software engineering, data engineering, or AI/ML delivery, including at least 4 years in customer-facing, consulting, or field engineering roles.
• Demonstrated success building and deploying enterprise-scale AI/ML applications into production.
• Deep full-stack engineering capabilities, including strong Python expertise plus experience with Node.js or Go, React or Vue, and SQL/NoSQL databases.
• Hands-on experience with LLMs, prompt engineering, vector databases, data pipelines, application dashboards, RAG architectures, and agent orchestration frameworks.
• Strong DevOps expertise with Docker, Kubernetes, CI/CD, GPU infrastructure, and cloud-native deployment practices.
• Experience integrating heterogeneous enterprise systems, including ERP platforms, data warehouses, data lakes, and streaming architectures.
• Experience with enterprise AI platforms such as Palantir Foundry and AIP, ontology modeling, Uniphore BAIC, or comparable technologies.
• Knowledge of SLM fine-tuning, model distillation, RLHF, and AI evaluation methodologies.
• Demonstrated experience building agentic AI solutions involving multi-agent systems, tool use, and autonomous workflow orchestration.
• Familiarity with GPU environments such as NVIDIA H100/B200 and InfiniBand, as well as private cloud technologies including OpenStack or VMware.
• Experience with knowledge graphs, semantic modeling, and ontology-driven data management is desirable.
• Ability to translate ambiguous customer requirements into practical engineering plans while working under demanding timelines.
• Excellent communication and presentation skills, including confidence working with C-suite stakeholders, leading technical workshops, and collaborating across functions.
• Prior experience in technology consulting, AI startups, Forward Deployed Engineering, Solutions Engineering, or similar customer-centric technical roles.
• Domain expertise in areas such as financial services, healthcare, supply chain, defense, energy, or manufacturing is a plus.

Benefits

• Annual compensation range of $207,957 to $305,003.60, depending on geographic market, job-related knowledge, skills, and experience.
• Potential annual bonus or incentive compensation.
• Potential equity awards.
• Employee Stock Purchase Plan (ESPP) eligibility.
• Fully remote work-from-home arrangement within the United States.
• Up to 25% travel for customer engagements.
• Opportunity to work on advanced enterprise AI initiatives with direct customer impact.
• Access to a broad technology ecosystem spanning AI, data, cloud, applications, and security.
• Strong opportunities for technical leadership, mentoring, knowledge sharing, and professional growth.
• Inclusive, collaborative environment that values expertise, customer focus, agility, innovation, and diverse perspectives.
• Opportunity to build reusable AI architectures and influence future platform and product capabilities.