Bloomberg

Bloomberg Law is changing the legal industry by delivering the most sophisticated legal tech platform on the market with a focus on automation, analytics, and real-time answers, including AI agents that assist legal professionals in complex research and drafting workflows. Our goal is to become an indispensable tool for legal professionals by supporting their day-to-day tasks and providing solutions that help them get real-time answers and better serve their clients.

The AI Ops engineer will be part of the Platform Engineering group within BLAW that develops and supports cloud native solutions and tools to deploy and operate BLAW products at scale on public cloud (AWS) environments. The team focuses on implementing platform-as-a-service (PaaS) frameworks, tools and workflows to accelerate product development. As an AI Ops engineer at Bloomberg Law, your mission is to design and build reliable and scalable cloud solutions to run diverse workloads on AWS. Our culture of diversity, intellectual curiosity, methodical problem solving and openness in a blameless environment are keys to our success. A good fit for our team is a person who is self-motivated, proactive, a good collaborator and comfortable with ambiguity.

Legal AI is an exciting and rapidly evolving field. If you are interested in working with a highly collaborative team to develop innovative solutions and make a big impact, please apply!

We'll trust you to:

• Work closely with ML Engineers and application engineers responsible for deploying ML models to have a good understanding of their MLOps needs to speed up ML Development.

• Collaborating with internal AI platform teams to understand availability of internal tools as well as tools available in AWS.

• Leverage open source tools and building frameworks and components to improve and scale our Serving and ML platform.

• Be a partner to Application teams and ML Engineers in designing cost and compute-optimal workflows for their use cases.

• Build and maintain infrastructure as code (IaC) in the cloud, that can scale when needed.

• Build and extend internal agent platforms, including tool orchestration, execution environments, and infrastructure for agentic AI workflows.

• Provide documentation and templates to make the onboarding the new workflows easy and seamless.
  
  

You'll need to have:

• 4+ years of experience programming in OOP (Java/Python)

• Proficiency with AWS (EC2, S3, SageMaker)

• A degree in Computer Science, Engineering or related technology field/Equivalent Experience

We'd love to see:

• Working knowledge of ML Development Lifecycle, experience in developing MLOps solutions and working with machine learning teams

• Familiarity of common ML frameworks such as PyTorch, Tensorflow, and Scikit-learn

• Prior experience with container technologies like Docker, Kubernetes, Buildpacks, etc.

• Experience with optimizing model performance on CPUs, GPUS (embedded hardware optimization is a plus)

• Curiosity to solve new problems and keep learning new technologies.

• Experience with agentic AI architectures and platforms, including agent harnesses, tool orchestration, code execution modes, sandboxed environments, and skill/plugin systems. Familiarity with how agents interact with filesystems, manage context, and chain tools to complete multi-step tasks. Understanding of patterns for building reliable agent workflows such as human-in-the-loop checkpoints, structured output handling, and runtime isolation is a strong plus.