Machine Learning Engineer

Serve as a lead developer for AI/ML within the infrastructure group across several applications.

Transform rapid prototypes into production-grade AI/ML systems that generate real return on investment.

Identify repeatable field patterns and technical friction points within MLP’s AI stack, converting them into reusable modules such as LLM routing and inference optimization on dedicated hardware.

Provide technical leadership in transitioning traditional development practices to AI-augmented approaches.

Design and implement AI infrastructure to support federated agent development and distributed deployment across the organization.

Manage and orchestrate AI agents to generate architecturally sound code, ensuring adherence to enterprise standards and best practices.

Build high-performance evaluation pipelines and observability frameworks to ensure agentic systems meet rigorous requirements for accuracy, safety, and latency.

Collaborate with development teams to integrate AI-powered tools into existing workflows and CI/CD pipelines.

Act as a deployed engineer, traveling to different teams across the firm to teach AI infrastructure best practices and guide adoption.
דרישות התפקיד
Education: 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.

AI Infrastructure: Experience with AI/ML infrastructure, agent orchestration, or similar AI-powered development tools.

Programming Skills: Proficiency in Python and PyTorch, or a similar deep learning framework.

Enterprise Architecture: Strong understanding of enterprise software architecture patterns and distributed systems design.

Containerization: Experience with Docker, Kubernetes, and container orchestration in production environments.

Communication: Strong communication and teaching skills, with the ability to work as a deployed engineer across different teams.

Preferred Qualifications

Cloud Platforms: Experience with cloud platforms (AWS, Azure, or GCP) and infrastructure-as-code tools.

Experience with MLOps and production ML workflows, including training pipelines, model versioning, deployment, monitoring, drift detection, and performance optimization in production environments.

Experience building agentic AI systems using frameworks such as LangGraph, LangChain, and PydanticAI, including LLM integration, tool calling, structured outputs, and Model Context Protocol (MCP) implementations.