Sr Engineer - MLOps Platform

Target • Brooklyn Park, NC

12d • Hybrid

About The Position

As a Senior Engineer, you serve as a specialist in the engineering team that supports the product. You help develop and gain insight in 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. You show good problem solving skills and can help the team in triaging operational issues. You leverage your expertise in eliminating repeat occurrences. 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.

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.

Nice To Haves

• 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.

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.

Benefits

• comprehensive health benefits and programs, which may include medical, vision, dental, life insurance and more
• 401(k)
• employee discount
• short term disability
• long term disability
• paid sick leave
• paid national holidays
• paid vacation

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed