AI Architect

AI Architect

Location:

Colmar, PA / Troy, MI / Milford, OH / Atlanta, GA / Edison, NJ

Duration:
Full Time

Job Description:

Large Language Models (LLMs) and agent frameworks (Lang Chain, CrewAI, Auto Gen, or similar)

RAG (Retrieval-Augmented Generation) architectures and vector databases

Prompt engineering and tool integration using APIs, knowledge graphs, or plugins

Model deployment via Azure AI Foundry, AWS Bedrock, Anthropic Claude, or similar platforms

Containerization and CI/CD pipelines for AI (Docker, Kubernetes, MLflow)

Strong programming experience in Python and familiarity with frameworks such as PyTorch, Tensor Flow, FastAPI, or Streamlit.

Proven ability to translate business goals into AI architecture and measurable outcomes.

Experience in Agentic AI systems, including autonomous agents, multi-agent collaboration, or AI orchestration frameworks.

Experience building AI copilots, assistants, or domain-specific agents for enterprise functions (e.g., IT ops, HR, manufacturing, or customer support)

Ability to work across different hyperscaler platforms such as AWS Bedrock, Azure Foundry, GCP Vertex AI etc.

Roles & Responsibilities:

Design AI Solutions

Design end-to-end AI architectures integrating LLMs, multi-agent frameworks, RAG pipelines, and workflow orchestration.

Define scalable and secure architectures for Gen AI and Agentic AI applications, including prompt-driven systems, autonomous agents, and AI copilots.

Design Intelligent modular architecture using MCP, A2A, Autogen etc.

Lead technical evaluations and selection of models (e.g., GPT, Claude, Gemini, LLaMA, Mistral) and vector databases

AI Strategy and Roadmap

Develop the AI technology roadmap aligned with organizational goals and data strategy.

Identify opportunities to embed AI across business workflows, improving efficiency, experience, and decision-making.

Implementation and Governance

Partner with engineering teams to operationalize AI models using Lang Chain, llama

Index, Vercel AI SDK, or similar frameworks.

Establish guardrails for model safety, prompt reliability, and ethical AI governance.

Define MLOps and AIOps practices for continuous improvement, monitoring, and lifecycle management of AI solutions.

Innovation and Experimentation

Evaluate and prototype new tools and techniques in autonomous agents, synthetic data generation, multi-modal models, and AI orchestration.

Collaborate with product and business teams to conceptualize AI-powered assistants, copilots, and automation flows.

Leadership and Mentorship

Provide technical leadership to data scientists, prompt engineers, and AI developers.

Promote a culture of innovation, experimentation, and measurable business impact through AI.