What are the responsibilities and job description for the AI Solutions Architect position at Virtusa?
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
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
- Azure AI search : semantic, keyword & hybrid search
- Azure document intelligence
- Multi-threading in Python: nice to have
- AsyncIO
- 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.