AI Solutions Architect

The AI Solutions Leader defines the technical direction for AI, ML, and data-driven capabilities across the enterprise.

The role shapes the architecture for intelligent, cloud-native solutions leveraging machine learning, LLMs, automation, and modern data platforms while ensuring security, scalability, compliance, and operational resilience.

Key Responsibilities
• Act as a subject matter expert and internal champion for AI capabilities.
• Hands on experience in LangChain & LangGraph 1.2.x
• Good understanding on agentic design patterns (workflows and multi agentic patterns - supervisor, sub agents and swarm)
• Writing evaluations to agentic systems. (Metric based and LLM as judge)
• Hands on experience in MCP servers and MCP clients.
• Azure AI foundry stack : nice to have
o Azure AI search : semantic, keyword & hybrid search
o Azure document intelligence
• Multi-threading in Python: nice to have
o AsyncIO
o Non blocking programming.
• Lead end-to-end solutioning of AI agents and agentic workflows, including design, development, testing, and deployment.
• Build business cases, conduct process reviews, perform data analysis, and document business requirements.
• Apply prompt and context engineering techniques to optimize AI model performance.
• Work with cutting edge Generative AI models such as OpenAI (ChatGPT), Anthropic, Microsoft, and Meta (LLaMA).
• Develop and deploy AI solutions using Python, Java, and frameworks like FastAPI, integrating with APIs and App Engine.
• Implement and manage CI/CD pipelines for efficient and secure deployment.
• Ensure secure handling of API keys, passwords, and tokens across environments.
• Use Docker for containerization and GitHub for version control and collaborative development.
• Conduct testing of AI agents and workflows to ensure reliability, accuracy, and performance.
• Collaborate with cross-functional teams to identify business problems and apply AI-driven solutions.
• Mentor Operations staff on AI solutioning, prompt design, and best practices.
• Drive AI adoption across the Operations Practice through enablement sessions, bootcamps, and strategic initiatives.
• Execute rapid response AI projects with a focus on delivery and measurable business impact.