Lead Forward Deployed Engineer (FDE) - AI Platforms (ML, LLMs, APIs, ML Ops)

Target • Brooklyn Park, MN

1d • $132,000 - $238,000 • Hybrid

About The Position

The Lead Forward Deployed Engineer (FDE) will accelerate adoption of Target’s enterprise AI platform by partnering with product and engineering teams to solve complex technical challenges, enable platform capabilities, and help teams build and operate production-grade AI applications. The role works across AI engineering, platform engineering, and product development, applying expertise in software architecture, distributed systems, LLMs, and agentic systems. It combines hands-on engineering with technical guidance, solution architecture, and development of reusable tools and solutions that improve platform adoption.

The position learns from real-world implementations and uses those insights to improve the broader AI platform. It collaborates across engineering teams to identify recurring challenges, reduce adoption friction, and translate successful implementation patterns into scalable platform capabilities. The Lead FDE independently designs and implements production-grade architectures, guides complex engineering efforts across teams and systems, helps teams navigate architectural tradeoffs, and connects technical decisions to long-term platform and product outcomes.

The role is based on a Hybrid/Flex for Your Day arrangement at Target HQ in Minnesota, with remote or hybrid arrangements potentially considered based on Target’s needs. Work must be performed within the country of the primary work location unless otherwise prescribed by Target.

Requirements

• A four-year degree or equivalent experience.
• At least 5 years of software development experience, with strong hands-on engineering and programming skills.
• Expertise in software architecture, design, distributed systems, APIs, cloud-native technologies, and modern engineering practices.
• Demonstrated programming proficiency in languages such as Java, Python, or Kotlin.
• Hands-on experience building and productionizing AI applications using LLMs, agentic architectures, model APIs, retrieval/context patterns, tool integration, and evaluation frameworks.
• Ability to lead complex technical initiatives from design through production, guide architectural decisions, and navigate ambiguous engineering challenges across teams or systems.
• Experience with production engineering practices, including CI/CD, testing, observability, reliability, performance, security, and operations.
• Experience developing reusable engineering patterns, technical tooling, reference implementations, APIs, SDKs, or platform capabilities that improve platform adoption and engineering efficiency.
• Ability to provide technical guidance, influence engineering decisions, and improve engineering practices through collaboration, architecture reviews, and knowledge sharing.
• Effective communication and technical judgment, including the ability to evaluate tradeoffs and collaborate across product, engineering, and platform teams.

Responsibilities

• Lead complex technical engagements that accelerate enterprise AI platform adoption, combining solution architecture, hands-on engineering, and technical guidance to move teams from experimentation to production.
• Design, build, and productionize AI solutions, reference implementations, technical tooling, and platform-support capabilities using LLMs, agentic architectures, enterprise context and retrieval, model APIs, and tool integrations.
• Guide technical design and implementation across engineering efforts, helping teams navigate architectural decisions, troubleshoot challenges, and develop scalable, maintainable solutions.
• Apply software engineering and distributed systems expertise to improve the reliability, security, performance, observability, and operational readiness of production AI systems.
• Identify recurring implementation challenges, integration gaps, and platform limitations, and collaborate with platform engineering to develop reusable patterns, APIs, SDKs, frameworks, and shared capabilities.
• Help establish and improve engineering practices across engagements, including architecture reviews, code quality, testing, evaluation, deployment, and production operations.
• Collaborate across product, engineering, and platform teams to evaluate technical approaches, share implementation learnings, and improve adoption and use of enterprise AI capabilities.
• Maintain technical knowledge in areas of expertise and stay current with evolving AI and software engineering technologies through formal training and self-directed learning.

Benefits

• Eligible team members and dependents may receive comprehensive health benefits and programs, which may include medical, vision, dental, and life insurance.
• 401(k).
• Employee discount.
• Short-term disability and long-term disability.
• Paid sick leave.
• Paid national holidays.
• Paid vacation.
• Financial, education, and well-being benefits.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior