Principal ML Engineer

Role:

Principal ML Engineer Location:

Hybrid -

Raleigh, NC Contract:

6+ months

Contract Exp:

12+ years

JOB DESCRIPTION

A leading business solutions and analytics organization is seeking a highly skilled Principal ML Engineer I to join its growing AI and Machine Learning team. This role is ideal for a hands-on engineering leader with deep expertise in modern AI systems, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic architectures, and cloud-native ML platforms. The ideal candidate will drive the architecture, development, and deployment of scalable AI/ML solutions while partnering with cross-functional teams to deliver production-ready intelligent systems aligned with business objectives. Key Responsibilities Architect, design, and develop end-to-end AI/ML solutions leveraging LLMs, RAG pipelines, and agentic AI systems. Build, train, optimize, and deploy machine learning and generative AI models using complex, high-dimensional datasets. Develop scalable AI applications and services using modern software engineering and MLOps best practices. Design and implement vector database integrations and semantic retrieval systems. Collaborate with engineering, product, and data teams to align AI architectures with technical and business requirements. Lead technical strategy and provide mentorship on AI engineering standards, model deployment, and cloud scalability. Improve predictive capabilities using techniques such as deep learning, generative modeling, neural networks, and NLP. Develop production-grade ML pipelines, model monitoring, testing frameworks, and CI/CD workflows. Ensure reliability, scalability, security, and performance optimization of AI platforms in cloud environments. Evaluate emerging AI technologies and recommend innovative solutions to enhance enterprise AI capabilities. Required Qualifications 8+ years of experience in Machine Learning, AI Engineering, or Software Engineering roles.

Deep expertise in:

Large Language Models (LLMs) Retrieval-Augmented Generation (RAG) Agentic AI systems Prompt engineering and AI orchestration frameworks Hands-on experience with MCP (Model Context Protocol) and vector databases. Strong experience with cloud platforms such as AWS, Azure, or Google Cloud Platform. Strong programming expertise in Python. Solid background in AI architecture, MLOps, and scalable software engineering practices. Experience building and deploying enterprise-grade AI/ML systems in production environments. Strong understanding of data engineering concepts, distributed systems, and ML lifecycle management. Experience with APIs, microservices, containerization, and cloud-native application development. Excellent communication and leadership skills with the ability to guide technical teams. Preferred Qualifications Experience with GoLang. Experience with LangChain, LangGraph, AutoGen, CrewAI, or similar AI orchestration frameworks. Familiarity with Kubernetes, Docker, Terraform, or infrastructure-as-code tools. Exposure to advanced NLP, multimodal AI, or reinforcement learning systems. Experience working in large-scale enterprise or analytics-driven environments. What You ll Bring Strong architectural thinking and problem-solving skills. Ability to lead complex AI initiatives from concept through production deployment. Passion for innovation in Generative AI and intelligent systems. Ability to work effectively in a collaborative hybrid environment.