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About the Role

The AI Engineer / Forward Deployed Engineer designs, builds, integrates, and operates production-grade AI solutions to solve real business problems within complex enterprise environments. This role blends hands-on software engineering, AI solution architecture, stakeholder engagement, and end-to-end delivery ownership, working closely with operational problems to translate business needs into deployed AI-enabled workflows.

Responsibilities

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.
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.
Build and tune LLM workflows, prompt strategies, schema-driven extraction, tool-calling patterns, agent orchestration, evaluation loops, and human-in-the-loop controls.
Integrate AI solutions with enterprise systems, APIs, data platforms, document repositories, workflow tools, observability platforms, and identity and access management services.
Ensure AI solutions meet enterprise standards for reliability, scalability, latency, maintainability, cost control, logging, monitoring, and operational support.
Create evaluation datasets, test harnesses, validation tools, regression checks, and quality review workflows to measure accuracy, extraction quality, hallucination risk, and business usefulness.
Define solution architecture, engineering standards, reusable patterns, and implementation approaches for AI-enabled platforms and services.
Work with engineering, data, and business teams to prepare structured and unstructured data, improve metadata, design retrieval strategies, and identify gaps in source content.
Embed access controls, audit logging, data protection, responsible AI controls, security review, and compliance requirements into the AI delivery lifecycle.
Support users through demos, pilots, training, feedback loops, documentation, and iterative improvement so that deployed AI solutions create measurable business value.

Requirements

Strong enterprise engineering background.
Deep expertise across modern full-stack technologies (.NET, Azure, SQL, React/Angular).
Experience in distributed systems.
Experience in observability.
Experience with AI tooling such as LLMs, retrieval pipelines, and agentic workflows.
Ability to mentor others.
Ability to resolve production challenges.
Ability to scale prototypes into robust, enterprise-grade solutions.

Skills

.NET
Azure
SQL
React
Angular
Distributed Systems
Observability
LLMs
Retrieval Pipelines
Agentic Workflows
Foundation Models
Retrieval-Augmented Generation
Orchestration Frameworks
Enterprise Integration
API Integration
Data Platforms
Document Repositories
Workflow Tools
Observability Platforms
Identity and Access Management
Reliability
Scalability
Latency Management
Maintainability
Cost Control
Logging
Monitoring
Operational Support
Evaluation Datasets
Test Harnesses
Validation Tools
Regression Checks
Quality Review Workflows
Solution Architecture
Engineering Standards
Reusable Patterns
Implementation Approaches
Data Preparation
Metadata Improvement
Retrieval Strategies
Security
Privacy
Governance
Access Controls
Audit Logging
Data Protection
Responsible AI Controls
Security Review
Compliance
User Support
Demos
Pilots
Training
Feedback Loops
Documentation
Iterative Improvement

Experience Level

Experienced Solutions Architect
Staff Engineer
Technical Lead