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

As a Lead Forward Deployed Engineer - AI Platforms (FDE), you will help accelerate the adoption of Target’s enterprise AI platform by working with product and engineering teams to solve complex technical challenges, enable platform capabilities, and help teams build and operate production-grade AI applications. You will operate at the intersection of AI engineering, platform engineering, and product development bringing technical expertise in software architecture, distributed systems, LLMs, and agentic systems. You will combine hands-on engineering with technical guidance, solution architecture, and development of reusable tools and solutions that improve how teams adopt and use the enterprise AI platform

A key part of this role is learning from real-world implementations and using those insights to improve the broader AI platform. You will collaborate across engineering teams to identify recurring challenges, address adoption friction, and help translate successful implementation patterns into scalable platform capabilities.

As a Lead FDE Engineer, you bring broad technical expertise and help guide complex engineering efforts across teams and systems. You can independently design and implement production-grade architectures while improving the effectiveness of others through technical guidance, engineering practices, and collaborative problem-solving. You help teams navigate architectural tradeoffs, raise engineering quality, and connect technical decisions to long-term platform and product outcomes.

As a Lead Engineer within Forward Deployed Engineering, you’ll:

• Lead complex technical engagements that accelerate enterprise AI platform adoption, combining solution architecture, hands-on engineering, and technical guidance to help teams move 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 complex architectural decisions, troubleshoot challenges, and develop scalable, maintainable solutions
• Apply software engineering and distributed systems expertise to improve the reliability, security, performance, observability, and op readiness of production AI systems
• Identify recurring implementation challenges, integration gaps, and platform limitations, collaborating 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 prod ops
• Collaborate across product, engineering, and platform teams to evaluate technical approaches, share implementation learnings, and improve how teams adopt and use enterprise AI capabilities

Core responsibilities of this job are described within this job description. Job duties may change at any time due to business needs.

• 4-year degree or equivalent experience
• 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 proficiency programming in Java, Python, Koltin, etc.
• Hands-on experience building and productionizing AI apps using LLMs, agentic architectures, model APIs, retrieval/context patterns, tool integration and evaluation frameworks
• Demonstrated 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 ops
• 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, with the ability to evaluate tradeoffs and collaborate across product, engineering, and platform teams
• Maintains technical knowledge within areas of expertise and stays current with rapidly evolving AI and software engineering technologies through formal training and self-directed learning.

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