AI Solution Architect / Lead AI Engineer
US client via staffing partner · Washington, DC
- Location
- Washington, DC · Hybrid
- Salary band
- Band not stated
- Type
- Contract
- Level
- Senior
- Work authorization
- Not stated
Stack
GenAI · RAG · AI Agents · LLMs · Vector Databases · Agent Orchestration · Prompt Engineering · Python · Azure OpenAI · Azure AI Search · LangChain · LangGraph · Semantic Kernel · Databricks · REST APIs · Data Engineering · Knowledge Graphs · Semantic Search · Copilot Studio · MCP
About the role
AI Solution Architect / Lead AI Engineer DC Washington - Hybrid 1. AI Solution Architect / Lead AI Engineer - 8+ years of software development experience with at least 3 years delivering GenAI, RAG, AI Agents, or Copilot-based solutions. - Experience designing end-to-end AI architectures including LLMs, vector databases, agent orchestration, prompt engineering, and multi-agent workflows. 2. Data Engineering & Knowledge Processing - Strong experience ingesting and transforming structured and unstructured data (PDFs, Excel, surveys, reports, APIs). - Hands-on expertise in document parsing, metadata extraction, data standardization, data quality validation, and knowledge graph or semantic search implementations. 3. Modern AI Technology Stack - Proficiency in Python, Azure OpenAI, Azure AI Search, LangChain/LangGraph, Semantic Kernel, Databricks, and REST APIs. - Experience building conversational applications with grounded responses, source citations, workflow automation, and analytical output generation (charts, reports, dashboards). 4. Enterprise Architecture & Governance - Experience delivering enterprise-grade solutions with authentication, authorization, data privacy, model governance, security reviews, and production readiness requirements. - Ability to design solutions that support auditability, human validation, explainability, and confidence scoring. 5. Business Domain Translation & Agile Delivery - Proven ability to work directly with business users, translate complex workflows into AI use cases, and rapidly iterate prototypes into production solutions. - Experience supporting analytics, research, data modeling, or knowledge management initiatives with strong stakeholder engagement skills. Nice-to-Have - Experience with MCP (Model Context Protocol), agentic AI platforms, Copilot Studio, and multi-agent orchestration. - Exposure to economics, financial analysis, statistical modeling, or research-oriented workflows. - Experience building "digital analyst" or decision-support assistants rather than simple FAQ chatbots.
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