TekDallas
Principal ML Engineer
Raleigh, North Carolina, USA · Principal · Contract
Sponsorship not specifiedDetected 70 days ago
GoDistributed SystemsVector DatabasesAWSGCPAzureCloud PlatformsDockerKubernetesTerraformCI/CDMachine LearningDeep LearningData EngineeringNLPLLMsRAGAgentic AIMLOpsLangGraphAI OrchestrationLeadershipCommunicationProblem Solving
About the role
- Improve predictive capabilities using techniques such as deep learning, generative modeling, neural networks, and NLP.
Responsibilities
- Architect, design, and develop end-to-end AI/ML solutions leveraging LLMs, RAG pipelines, and agentic AI systems.
- 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.
- Develop production-grade ML pipelines, model monitoring, testing frameworks, and CI/CD workflows.
Requirements
- 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.
- 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.
Nice to have
- Experience with GoLang.
- Experience with LangChain, LangGraph, AutoGen, CrewAI, or similar AI orchestration frameworks.
- Familiarity with Kubernetes, Docker, Terraform, or infrastructure-as-code tools.
- Experience working in large-scale enterprise or analytics-driven environments.
- What You’ll Bring Strong architectural thinking and problem-solving skills.
- Passion for innovation in Generative AI and intelligent systems.
- 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.
Skills
- Ensure reliability, scalability, security, and performance optimization of AI platforms in cloud environments.
Benefits
- Build, train, optimize, and deploy machine learning and generative AI models using complex, high-dimensional datasets.
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