Description
As a Machine Learning Engineer at Millennium, you will lead the development of AI and machine learning capabilities within the infrastructure group across multiple applications.
You will transform rapid prototypes into production-grade AI and machine learning systems that deliver measurable business value.
Your responsibilities will include:
- Identifying repeatable patterns and technical friction points across Millennium's AI stack, translating them into reusable capabilities such as LLM routing and dedicated-hardware inference optimization.
- Designing and implementing scalable AI infrastructure that supports federated agent development and distributed deployment across the organization.
- Orchestrating AI agents across distributed systems and service buses, ensuring generated code aligns with enterprise architecture standards and best practices.
- Building high-performance evaluation pipelines and observability frameworks to assess agentic systems against rigorous accuracy, safety, and latency requirements.
- Advancing the adoption of AI-augmented development practices by providing technical leadership and integrating AI-powered tools into engineering workflows and CI/CD pipelines.
- Partnering directly with teams across the firm to teach AI infrastructure best practices and guide successful adoption.
To succeed in this role, you will need:
- A Bachelor's degree with 6+ years of experience, Master's degree with 4+ years of experience, or PhD with 2+ years of experience in Computer Science, Data Science, Machine Learning, or a related field.
- Experience building or operating AI/ML infrastructure, agent orchestration platforms, or comparable AI-powered development tools.
- Proficiency in Python and PyTorch or a similar deep learning framework.
- Strong understanding of enterprise software architecture patterns and distributed systems design.
- Production experience with Docker, Kubernetes, and container orchestration.
- Strong communication and teaching skills, with the ability to partner effectively with teams across the organization as a deployed engineer.
- Experience with cloud platforms such as AWS, Azure, or GCP; infrastructure-as-code tools; and MLOps practices, including model training, versioning, deployment, monitoring, drift detection, and performance optimization.
- Experience building agentic AI systems using frameworks such as LangGraph, LangChain, or PydanticAI, including LLM integration, tool calling, structured outputs, MCP implementations, vector databases, embedding pipelines, and retrieval-augmented generation architectures.