Generative AI Engineer

Title: Agentic AI EngineerDuration: 6+ Months - Long Term Location: Washington, DC 20433Hybrid Onsite: 4 days per week from Day 1, with a full transition to 100% onsite anticipated soonPosition Summary: Client is seeking a highly experienced Azure/AWS Agentic AI/ML Engineer to design, develop, deploy, and optimize enterprise-scale generative AI and agentic AI solutions that support critical business operations and global development initiatives.The ideal candidate will possess extensive experience building AI-powered applications using Azure AI services, large language models (LLMs), retrieval-augmented generation (RAG), multi-agent frameworks, and modern MLOps practices. The engineer will collaborate closely with business stakeholders, solution architects, data engineers, and cybersecurity teams to deliver secure, scalable, and production-ready AI systems.Key Responsibilities:Agentic AI Architecture and Development: Design, develop, and deploy enterprise-scale agentic AI solutions using Azure & AWS AI services.Build single-agent and multi-agent architectures using frameworks such as LangGraph, LangChain, CrewAI, Semantic Kernel, AutoGen, and Model Context Protocol (MCP).Develop autonomous workflows, orchestration frameworks, reasoning engines, and memory management capabilities.Design and implement tool-calling capabilities and agent communication mechanisms.Build intelligent assistants capable of interacting with structured and unstructured enterprise data sources.Generative AI and Large Language Models:Integrate and optimize OpenAI, Azure OpenAI, Anthropic Claude, Gemini, Llama, and other foundation models.Develop prompt engineering strategies, evaluation frameworks, and context management solutions.Implement RAG pipelines utilizing vector databases and knowledge graphs.Fine-tune models and optimize inference performance for production environments.Machine Learning Engineering: Design, train, deploy, and monitor machine learning models using AWS/Azure Machine Learning.Develop scalable data pipelines supporting model training, inference, and evaluation.Implement feature engineering, model monitoring, drift detection, and performance optimization strategies.Collaborate with data engineering teams to process large-scale datasets.Required Qualifications: Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, Mathematics, or a related field.6+ years of software engineering and machine learning experience.3+ years of hands-on experience implementing enterprise AI solutions.Strong proficiency in C#.Net, Python, SQL, and REST APIs.Extensive experience with Azure and AWS cloud services.Strong understanding of transformer architectures, embeddings, vector databases, and semantic search.Experience building production-grade AI applications using RAG and agentic frameworks.Strong knowledge of distributed systems, microservices, and containerization technologies.“Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of – Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.”