Sr Engineer - AI Platforms - Forward Deployed Engineer (FDE)

Target•Brooklyn Park, MN

2d•$98,000 - $176,000•Hybrid

About The Position

As a Senior Engineer – AI Platforms, Forward Deployed Engineering (FDE), you will be part of a team focused on accelerating the adoption of Target's enterprise AI platforms by working directly with engineering and product teams to solve complex, high-value AI problems. You will operate at the intersection of AI engineering, platform engineering, and product development, bringing deep technical expertise directly to teams building AI-powered experiences. You will help teams move from experimentation to production by designing and implementing solutions, integrating enterprise AI platform capabilities, and establishing scalable engineering patterns. A key part of this role is learning from real-world implementations and using those insights to improve the broader AI platform and builder experience. You will identify recurring technical challenges and work with platform teams to turn successful implementation patterns into reusable components, APIs, SDKs, frameworks, tooling, reference implementations, and self-service capabilities. As a Senior Engineer, you serve as a specialist in the engineering team that supports the product. You help develop and gain insight into application architecture. You can distill an abstract architecture into concrete design and influence the implementation. You demonstrate expertise in applying appropriate software engineering patterns to build robust and scalable systems. You are an expert in programming and apply your skills in developing the product. You have the skills to design and implement architecture independently while influencing fellow engineers through software designs, implementation guidance, code reviews, and technical mentorship.

Requirements

4-year degree or equivalent experience.
5+ years of software development experience with strong hands-on engineering and programming skills.
Strong experience designing, building, testing, deploying, and operating production-grade applications, services, or distributed systems.
Experience building AI-enabled applications and working with technologies such as LLMs, model APIs, agentic frameworks, retrieval/context patterns, tool integration, or other modern AI capabilities.
Strong proficiency in one or more modern programming languages, with experience developing services and applications using modern frameworks and engineering practices.
Experience designing and consuming APIs, SDKs, libraries, frameworks, or platform capabilities.
Strong understanding of software architecture, distributed systems, APIs, scalability, reliability, performance, security, and production operations.
Experience with cloud-native development, containers, orchestration, CI/CD, observability, testing, debugging, and operational monitoring.
Ability to take ambiguous requirements, rapidly understand the problem space, and translate them into concrete technical designs and working software.
Ability to work across the full engineering lifecycle—from rapid prototyping and experimentation to hardened production systems.
Demonstrated ability to evaluate technical tradeoffs and balance speed of delivery with scalability, maintainability, operational excellence, and long-term platform needs.
Strong problem-solving skills with the ability to diagnose complex technical and operational issues and eliminate repeat occurrences.
Experience collaborating directly with product teams and engineers and working effectively across organizational and technical boundaries.
Ability to communicate complex technical concepts clearly and influence design and implementation decisions through technical expertise.
Demonstrated ability to mentor engineers, provide technical guidance, and raise engineering standards within a team.
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.

Responsibilities

Partner directly with engineering and product teams to design, build, and productionize AI-powered applications and experiences using Target's enterprise AI platform.
Develop production-grade solutions using generative AI, LLMs, agentic architectures, enterprise context and retrieval, model APIs, tools, and platform services.
Rapidly understand ambiguous business and engineering problems, translate them into technical designs, and build working solutions that demonstrate measurable value.
Lead technical implementation across the lifecycle—from exploration and prototyping through architecture, development, evaluation, deployment, observability, and production operations.
Apply strong software engineering and distributed systems principles to ensure AI solutions are scalable, reliable, secure, maintainable, and production-ready.
Work closely with AI Platform teams to integrate existing platform capabilities and identify gaps encountered while building real-world applications.
Identify patterns across FDE engagements and help convert repeatable solutions into reusable platform capabilities, APIs, SDKs, libraries, frameworks, templates, and reference architectures.
Improve the AI builder experience by reducing friction across development, experimentation, testing, evaluation, debugging, deployment, and production operations.
Build reference implementations and examples that demonstrate effective patterns for adopting enterprise AI capabilities.
Evaluate emerging AI technologies, frameworks, models, and developer tooling through research and proof-of-concept activities and recommend appropriate approaches based on scalability, maintainability, security, and cost.
Lead code reviews, design reviews, testing, debugging, and architecture discussions while maintaining high engineering quality and operational standards.
Diagnose complex production and integration issues, identify systemic causes, and develop solutions that prevent repeat occurrences.
Collaborate across platform engineering, application engineering, product, architecture, data science, and other teams to deliver solutions spanning organizational and technical boundaries.
Share technical learnings, patterns, and best practices from FDE engagements with the broader engineering community and help other engineers successfully adopt them.