Machine learning engineer

About the Role

Our client is seeking a Senior Machine Learning Engineer II to help build and scale advanced Agentic AI and Multi-Agent Systems that transform how professionals interact with information. This role goes beyond traditional machine learning engineering. The ideal candidate will have experience designing intelligent AI systems capable of reasoning, planning, retrieval, tool utilization, workflow orchestration, and autonomous task execution across large-scale knowledge environments. You will work closely with Applied Scientists, ML Engineers, Architects, Product Leaders, and Software Engineers to develop production-grade AI systems that leverage Large Language Models (LLMs), RAG architectures, vector search, agent frameworks, and emerging reasoning technologies.

Key Responsibilities

Agentic AI Development

• Design, build, and deploy production-scale multi-agent AI systems.
• Develop agent workflows capable of planning, reasoning, retrieval, tool utilization, validation, and task execution.
• Architect agent ecosystems utilizing specialized agent roles such as: Planner, Researcher, Critic, Verifier, Writer, Orchestrator.
• Implement shared memory, state management, context preservation, and agent communication frameworks.
• Develop guardrails and validation systems to improve reliability and reduce hallucinations.

Retrieval-Augmented Generation (RAG)

• Design and optimize enterprise-scale RAG architectures.
• Develop advanced retrieval strategies leveraging vector databases, semantic search, knowledge graphs, and metadata filtering.
• Improve grounding, citation accuracy, retrieval quality, and relevance.
• Optimize chunking strategies, embedding pipelines, and context management.

Machine Learning Engineering

• Build scalable AI and machine learning services deployed into production environments.
• Develop model evaluation frameworks for both traditional machine learning and LLM-based systems.
• Create automated testing pipelines for prompts, retrieval systems, agent workflows, and AI outputs.
• Fine-tune, evaluate, and optimize AI systems for performance, latency, quality, and cost.

AI Evaluation & Observability

• Define and measure success metrics for agentic AI systems, including task completion rates, accuracy, hallucination rates, retrieval effectiveness, cost efficiency, user satisfaction, and latency.
• Implement monitoring, observability, and evaluation frameworks for LLM applications.
• Develop processes for continuous improvement and model governance.

Research & Innovation

• Evaluate emerging AI technologies and frameworks.
• Investigate advances in Multi-Agent Systems, Agentic AI, Reasoning Models, LLM Orchestration, Knowledge Retrieval, and Autonomous AI Workflows.
• Contribute to architecture standards and AI platform strategy.
• Participate in proof-of-concepts and innovation initiatives.

Required Qualifications

• Bachelor's degree in Computer Science, Machine Learning, Data Science, Engineering, or related discipline.
• 6+ years of software engineering, machine learning engineering, or applied AI experience.
• 3+ years building production AI, LLM, or Generative AI solutions.
• Strong Python development experience.
• Experience developing production-grade Retrieval-Augmented Generation (RAG) systems.
• Experience building or supporting Agentic AI, AI orchestration, or multi-agent workflows.
• Strong understanding of Large Language Models (LLMs), Natural Language Processing (NLP), Information Retrieval, Semantic Search, Embeddings, Prompt Engineering, and Model Evaluation.
• Experience working with vector databases and retrieval platforms.
• Strong software engineering fundamentals including testing, CI/CD, observability, and scalable system design.

Preferred Qualifications

• Experience with agent frameworks such as LangGraph, AutoGen, CrewAI, Semantic Kernel, OpenAI Agents SDK, and LlamaIndex Workflows.
• Experience implementing multi-agent communication patterns, shared memory architectures, tool-calling agents, autonomous workflows, and human-in-the-loop systems.
• Experience with cloud platforms including AWS, Azure, or GCP.
• Familiarity with Kubernetes, Docker, MLOps, LLMOps, and AI observability platforms.
• Knowledge graph or enterprise search experience.
• Experience building AI systems in highly regulated or knowledge-intensive domains.

Pay and Benefits

The pay range for this position is $75.00 - $85.00/hr. Individual compensation offered for this position within this range will depend on many factors, including qualifications, skills, relevant experience, job knowledge, geographic location, internal equity, and other pertinent job-related factors.

Eligibility requirements apply to some benefits and may depend on your job classification and length of employment. Benefits are subject to change and may be subject to specific elections, plan, or program terms. If eligible, the benefits available for this temporary role may include the following: Medical, dental & vision, Critical Illness, Accident, and Hospital, 401(k) Retirement Plan – Pre-tax and Roth post-tax contributions available, Life Insurance (Voluntary Life & AD&D for the employee and dependents), Short and long-term disability, Health Spending Account (HSA), Transportation benefits, Employee Assistance Program, Time Off/Leave (PTO, Vacation or Sick Leave).