Forward Deployed AI Engineer

Location: Atlanta, GA — Hybrid / Onsite

THE OPPORTUNITY

We are looking for a Forward Deployed AI Engineer to work closely with business, technology and operational stakeholders in Atlanta. The role will turn high-value business opportunities into governed, production-ready AI and agentic solutions across engineering, manufacturing, service, modernization, supply chain and customer-facing operations.

The engineer will operate at the intersection of AI engineering, enterprise architecture and business process transformation—rapidly assessing use cases, defining solution patterns and governance, building or directing prototypes, and moving successful solutions into production.

WHAT YOU WILL OWN

AI opportunity-to-production pipeline — identify, prioritize and assess AI opportunities based on business value, technical feasibility, risk, data readiness and time-to-value.

Rapid AI assessments — lead focused 4–6-week assessments for selected use cases and convert successful concepts into actionable, costed delivery roadmaps.

Agent and solution patterning — determine the appropriate pattern for each use case, including knowledge assistants, workflow/agentic automation, decision support and higher-autonomy agents.

Enterprise AI architecture — design scalable solutions across Azure/cloud services, enterprise APIs, data platforms and client’s existing digital ecosystem; avoid unnecessary net-new tooling.

AI governance and responsible AI — establish practical standards for security, access, data protection, evaluation, human-in-the-loop controls, observability, cost management and production monitoring.

Delivery leadership — work with distributed engineering teams and delivery partners to set the technical direction, review implementations, remove blockers and accelerate production adoption.

Business and operational engagement — partner with engineering, manufacturing, field service, modernization, supply chain, sales and corporate functions to understand workflows and define measurable outcomes.

Value realization — establish adoption, productivity, quality, service, cost and other relevant KPIs and communicate outcomes to business and technology leadership.

EXAMPLE AI / GENAI USE CASE AREAS

Field service and maintenance: technician knowledge assistance, service-ticket summarization, troubleshooting guidance, predictive insights and parts/service recommendations.

Engineering and manufacturing: engineering knowledge assistants, document intelligence, quality analysis, root-cause support and workflow automation.

Supply chain and procurement: demand and inventory insights, supplier information analysis, sourcing support and procurement workflow automation.

Customer and commercial operations: proposal/RFP assistance, customer-service knowledge, contract/document analysis and sales enablement.

Enterprise functions: finance, HR, IT and other corporate workflows where AI can reduce manual effort, improve decision support or accelerate access to enterprise knowledge.

WHAT YOU BRING

8+ years of enterprise technology delivery experience, with 3+ years building AI, GenAI, intelligent automation or data-driven solutions in client-facing or embedded environments.

Strong hands-on experience with LLMs, agentic AI patterns, RAG, orchestration, evaluation, prompt engineering and enterprise API integration.

Experience with Azure AI / Azure OpenAI, Microsoft AI capabilities or comparable enterprise AI platforms strongly preferred.

Demonstrated experience taking AI solutions from discovery and prototype through production, including observability, evaluation, guardrails and operational support.

Strong understanding of enterprise data, APIs, cloud architecture, security and integration patterns.

Ability to translate business problems into practical AI solutions and balance value, feasibility, cost, risk and speed.

Experience working with distributed engineering teams, offshore delivery teams or systems-integration partners.

Strong stakeholder-management and communication skills, with the ability to work directly with senior business and technology leaders.

Experience in industrial, manufacturing, engineering, mobility, transportation, building systems, field service or another asset-intensive environment is highly relevant.

FIRST 90 DAYS — WHAT GOOD LOOKS LIKE

Complete rapid assessments for the first 1–3 priority AI opportunities, with clear business owners, technical approach, expected value, risks and costed delivery plans.

Establish practical AI architecture, governance, evaluation and observability standards aligned with enterprise technology and security requirements.

Deliver or launch the first production-ready AI/agentic use cases with measurable adoption and business outcomes.

Create reusable AI solution patterns, technical accelerators and documentation that can be leveraged by distributed engineering and delivery teams.

Establish a prioritized AI roadmap covering near-term production opportunities and the next wave of use cases.

 

LOCATION & ENGAGEMENT

Location: Atlanta, GA

Work Model: Hybrid / onsite collaboration with American stakeholders

Engagement: 12 months, extendable

Target Start: October 2026