AI Engineer/Forward-Deployed Engineer

The Role

The AI Engineer / Forward Deployed Engineer is responsible for designing, building, integrating, and operating production-grade AI solutions that solve real business problems inside complex enterprise environments. The role combines hands-on software engineering, AI solution architecture, customer or business stakeholder engagement, and end-to-end delivery ownership.

Unlike a traditional AI or software engineering role focused only on internal product backlogs, this role works close to the operational problem. The engineer translates ambiguous business needs into deployed AI-enabled workflows, connects enterprise systems and data sources, validates output quality, and ensures solutions are reliable, secure, cost-effective, and adopted by users.

This position is ideal for an experienced Solutions Architect, Staff Engineer, or Technical Lead with a strong enterprise engineering background and a passion for applying it to AI-enabled systems. You’ll bring deep expertise across modern full-stack technologies (.NET, Azure, SQL, React/Angular), along with experience in distributed systems, observability, and AI tooling such as LLMs, retrieval pipelines, and agentic workflows.
Acting as a bridge between business and technology, you’ll work across product, data science, architecture, and engineering teams—mentoring others, resolving production challenges, and scaling prototypes into robust, enterprise-grade solutions that deliver real impact.

The Responsibilities

• AI solution delivery: Design and build AI-enabled applications, copilots, agents, extraction pipelines, prediction interfaces, and decision-support tools using foundation models, retrieval-augmented generation, structured outputs, and orchestration frameworks.
• Forward deployed problem solving: Work directly with business teams, product owners, clients, or operational users to understand real workflows, constraints, data quality issues, and adoption barriers, then translate these into working technical solutions.
• LLM and agent engineering: Build and tune LLM workflows, prompt strategies, schema-driven extraction, tool-calling patterns, agent orchestration, evaluation loops, and human-in-the-loop controls.
• Enterprise integration: Integrate AI solutions with enterprise systems, APIs, data platforms, document repositories, workflow tools, observability platforms, and identity and access management services.
• Production engineering: Ensure AI solutions meet enterprise standards for reliability, scalability, latency, maintainability, cost control, logging, monitoring, and operational support.
• Evaluation and quality assurance: Create evaluation datasets, test harnesses, validation tools, regression checks, and quality review workflows to measure accuracy, extraction quality, hallucination risk, and business usefulness.
• Architecture and technical leadership: Define solution architecture, engineering standards, reusable patterns, and implementation approaches for AI-enabled platforms and services.
• Data and knowledge readiness: Work with engineering, data, and business teams to prepare structured and unstructured data, improve metadata, design retrieval strategies, and identify gaps in source content.
• Security, privacy, and governance: Embed access controls, audit logging, data protection, responsible AI controls, security review, and compliance requirements into the AI delivery lifecycle.
• Adoption and enablement: Support users through demos, pilots, training, feedback loops, documentation, and iterative improvement so that deployed AI solutions create measurable business value.