Sr. AI Architect

Ampcus Inc.•Chantilly, VA

About The Position

Ampcus Inc. is seeking an experienced Senior AI Architect to lead the design, development, and deployment of next-generation Generative AI and Agentic AI solutions. The ideal candidate will have deep expertise in Retrieval-Augmented Generation (RAG) pipelines, AI coding assistants such as Claude Code, OpenAI Codex, and Cline, and experience building autonomous AI agents capable of reasoning, planning, and executing complex business workflows. This role requires a hands-on technical leader who can define AI strategy, architect enterprise-grade AI platforms, guide engineering teams, and drive innovation across GenAI, LLMOps, and intelligent agent ecosystems.

Requirements

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
12 years of overall software engineering experience.
5 years in AI/ML solution architecture.
2 years building production-grade Generative AI applications.
Proven experience delivering enterprise-scale AI platforms.
Generative AI & LLMs: GPT-4/5, Claude, Gemini, Llama, Mistral, DeepSeek, Open-source LLM ecosystems.
RAG Technologies: LangChain, LlamaIndex, Vector databases, Embeddings, Semantic Search, Knowledge Graphs, Hybrid Retrieval.
Agentic AI: LangGraph, CrewAI, AutoGen, Semantic Kernel, MCP (Model Context Protocol), Tool Calling, Function Calling, Agent Memory Architectures.
Programming Languages: Python (Expert), JavaScript / TypeScript, C#, Java (Preferred).
Cloud Platforms: Microsoft Azure (Preferred), Azure AI Foundry, Azure OpenAI, AWS Bedrock, Google Vertex AI.
DevOps & MLOps: Docker, Kubernetes, GitHub Actions, Jenkins, Terraform, MLflow, Weights & Biases.
Databases: PostgreSQL, MongoDB, Cosmos DB, Redis, Neo4j, Vector Databases.

Nice To Haves

Preferred Certifications: Microsoft Certified: Azure AI Engineer Associate, Azure Solutions Architect Expert, AWS Machine Learning Specialty, Google Professional Machine Learning Engineer, Databricks Generative AI Certification.

Responsibilities

Design and implement enterprise-scale Generative AI solutions using Large Language Models (LLMs).
Architect advanced RAG-based systems incorporating vector databases, semantic search, enterprise knowledge repositories, and hybrid retrieval models.
Define scalable AI architectures covering data ingestion, embeddings, retrieval, orchestration, guardrails, observability, and evaluation frameworks.
Establish AI governance, security, compliance, and responsible AI practices.
Design and develop multi-agent and Agentic AI solutions capable of planning, reasoning, tool usage, memory management, and workflow orchestration.
Build autonomous agents using frameworks such as: LangGraph, CrewAI, AutoGen, Semantic Kernel, OpenAI Agents SDK, LangChain.
Implement agent collaboration, human-in-the-loop workflows, and agent monitoring mechanisms.
Architect and optimize Knowledge ingestion pipelines, Document chunking strategies, Embedding architectures, Query optimization, Re-ranking models, Hybrid search implementations.
Work with vector databases such as: Pinecone, Weaviate, Qdrant, Chroma, Azure AI Search, Elasticsearch/OpenSearch.
Lead adoption and integration of AI-assisted development tools including: Claude Code, OpenAI Codex, Cline, GitHub Copilot, Cursor.
Define standards and best practices for AI-driven software engineering and code generation workflows.
Architect AI-powered SDLC automation capabilities.
Build robust CI/CD pipelines for AI applications.
Implement: Model evaluation frameworks, Prompt management, Experiment tracking, Cost optimization, AI observability, Production monitoring.
Establish enterprise LLMOps standards and governance models.
Mentor architects, engineers, and AI specialists.
Define technology roadmaps and AI strategy aligned with business objectives.
Engage with stakeholders, product teams, and executive leadership to translate business challenges into AI solutions.
Evaluate emerging AI technologies and recommend adoption strategies.