Sr Engineer - MLOps Platform

Pay range: $98,000.00 - $176,000.00. Pay is based on several factors including labor markets, education, work experience, and certifications.

Benefits

• Comprehensive health benefits and programs for eligible team members and their dependents, including medical, vision, dental, and life insurance
• 401(k) retirement plan
• Employee discount
• Short-term and long-term disability coverage
• Paid sick leave
• Paid national holidays
• Paid vacation

Find competitive benefits from financial and education to well-being and beyond at Target Benefits.

About Us

Working at Target means helping all families discover the joy of everyday life. We bring that vision to life through our values and culture. Learn more about Target here.

About The Role

As a Senior Engineer, you serve as a specialist in the engineering team that supports the product. You help develop and gain insight into the application architecture. You can distill an abstract architecture into concrete design and influence the implementation. You show expertise in applying the 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 the architecture on your own but choose to influence your fellow engineers by proposing software designs, providing feedback on software designs, and/or implementation.

As a Sr Engineer on the MLOps Platform team, you will help design, build, and evolve an enterprise MLOps platform that enables teams to develop, deploy, and operate machine learning and Generative AI solutions at scale. You will combine strong software engineering and platform engineering fundamentals with an understanding of ML and AI workflows. You will partner with Data Scientists, ML Engineers, product managers, and platform teams to build secure, reliable, and easy-to-use capabilities across the AI/ML lifecycle. This is a hands-on engineering role focused on building platforms, services, and developer experiences that enable AI/ML teams to move from experimentation to production.

Responsibilities

• Design, build, test, and operate scalable services and capabilities for an enterprise MLOps platform
• Build APIs, microservices, and event-driven systems that support ML and Generative AI workflows
• Develop platform capabilities for model development, deployment, serving, monitoring, and lifecycle management
• Enable Generative AI use cases including LLMs, RAG, embeddings, vector search, and agentic applications through reusable platform capabilities
• Integrate with cloud AI/ML services, data platforms, model providers, and enterprise systems
• Build automation and self-service experiences that improve developer and Data Scientist productivity
• Implement observability, evaluation, governance, security, and reliability capabilities across the ML lifecycle
• Optimize platform services for scalability, availability, performance, and cost
• Apply strong engineering practices including automated testing, CI/CD, infrastructure automation, and operational excellence
• Collaborate across engineering, Data Science, product, security, and infrastructure teams and mentor other engineers through design and code reviews

Requirements

• 5+ years of professional software engineering experience building and operating production systems
• Strong proficiency in Java or a comparable object-oriented programming language; experience with Python is beneficial
• Experience with REST APIs, microservices, distributed systems, SQL/NoSQL databases, Docker, Kubernetes, Git, and CI/CD
• Experience building or supporting platforms, developer tooling, or infrastructure services
• Understanding of the machine learning lifecycle, including experimentation, training, deployment, serving, monitoring, and model management
• Familiarity with MLOps practices and technologies for production ML systems
• Experience with cloud platforms; GCP preferred
• Familiarity with Generative AI technologies including LLMs, RAG, embeddings, vector databases, and AI agents
• Experience with monitoring, observability, security, and reliability of production systems
• Ability to independently design and deliver scalable platform capabilities
• Strong communication and collaboration skills across engineering, Data Science, product, and platform teams

Desired Qualifications

• Experience building or operating an enterprise MLOps or AI platform
• Experience with cloud ML platforms such as Gemini Enterprise Agent Platform (Vertex AI) or equivalent technologies
• Experience with Kubernetes-based ML infrastructure and model serving
• Experience enabling Generative AI capabilities through shared platforms or services
• Experience designing self-service developer platforms, SDKs, APIs, or tooling

Schedule

This position will operate as a Hybrid/Flex for Your Day work arrangement based on Target’s needs. A Hybrid/Flex for Your Day work arrangement means the team member’s core role will need to be performed both onsite at the Target HQ MN location the role is assigned to and virtually, depending upon what your role, team, and tasks require for that day.