Machine Learning Engineer

TEKsystems•Raleigh, NC

8d•$75 - $85•Remote

About The Position

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. This position is ideal for engineers who have moved beyond basic prompt engineering and have experience building sophisticated AI applications capable of solving complex, multi-step business problems.

Requirements

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, Model Evaluation.
Experience working with vector databases and retrieval platforms.
Strong software engineering fundamentals including testing, CI/CD, observability, and scalable system design.

Nice To Haves

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

Responsibilities

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.
Design and optimize enterprise-scale RAG architectures.
Develop advanced retrieval strategies leveraging: Vector databases, Semantic search, Knowledge graphs, Metadata filtering, Hybrid retrieval approaches.
Improve grounding, citation accuracy, retrieval quality, and relevance.
Optimize chunking strategies, embedding pipelines, and context management.
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.
Define and measure success metrics for agentic AI systems, including: Task completion rates, Accuracy, Hallucination rates, Retrieval effectiveness, Cost efficiency, User satisfaction, Latency.
Implement monitoring, observability, and evaluation frameworks for LLM applications.
Develop processes for continuous improvement and model governance.
Evaluate emerging AI technologies and frameworks.
Investigate advances in: Multi-Agent Systems, Agentic AI, Reasoning Models, LLM Orchestration, Knowledge Retrieval, Autonomous AI Workflows.
Contribute to architecture standards and AI platform strategy.
Participate in proof-of-concepts and innovation initiatives.