Integrate Generative AI models, including LLMs, with external APIs, tools, and databases using secure and efficient orchestration patterns.

Design, develop, and deploy AI workflows and Agentic AI solutions, enabling the seamless orchestration of intelligent agents to plan and perform tasks while leveraging autonomous and/or human-in-the-loop paradigms.

Implement and optimize multi-agent systems, leveraging standards and protocols such as Model Context Protocol (MCP) and emerging frameworks for agent interoperability.

Develop evaluation frameworks, metrics, and checkpoints for agent autonomy, performance, and safety, ensuring compliance with moderation, security, and ethical standards.

Ensure robust AI agent operations by applying observability, monitoring, and MLOps best practices, facilitating reliable deployment pipelines and continuous performance optimization.

Collaborate closely with data experts, orchestrating AI model selection, tuning, and performance validation to meet specific agent-based application needs.

Communicate complex AI concepts, systems, and decisions effectively to technical and non-technical stakeholders, promoting transparency and trust in AI delivery.

Proven experience designing and deploying AI architectures, with expertise in Generative AI, NLP, LLM integration, and software engineering.

Strong background in building software platforms (Python/Django, Java/Spring, TypeScript/Express, etc.) capable of API integration and orchestration.

Strong understanding of the trade-offs between various generative AI models and the ability to choose the right model for specific use cases.

Hands-on experience with function-calling and tools integration into LLM models, leveraging frameworks such as Model Context Protocol (MCP).

Expertise in data embeddings, vector databases, and chunking strategies, understanding the trade-off between different options, and leveraging it to optimize data ingestion and application performance.

Experience using CI/CD tools (GitHub Actions, Jenkins, AWS CodeDeploy, Azure Pipelines) to streamline development and deployment workflows.

Hands-on experience deploying software on leading cloud platforms and utilizing AI tools like AWS Bedrock and Azure AI Services.

Experience leveraging evaluation frameworks (e.g., RAGAS, OpenAI Eval) and tools (e.g., DeepEval, LangSmith, Braintrust) to assess business and performance metrics of AI solutions.

Understanding of performance optimization, including the use of observability platforms, event tracking, and performance validation.

Practical knowledge of deploying AI solutions using cloud platforms like AWS, Azure, or GCP, utilizing services such as AWS Bedrock or Azure AI Services.

Excellent skills in prompt and context engineering, ensuring the usage of the right techniques to meet diverse project requirements.

Ability to communicate complex AI solutions and concepts effectively to technical and non-technical stakeholders.