About The Role

Strong hands-on experience with Python and production-grade AI/GenAI application development.
Must-have expertise in FastAPI for developing scalable, secure, and production-ready APIs.
Strong experience with LLMs, Prompt Engineering, RAG, Vector Databases, and Embeddings .
Hands-on experience designing and developing enterprise AI agents, conversational AI, and Agentic AI solutions .
Strong experience building RAG pipelines , including document processing, chunking, embeddings, vector search, retrieval, grounding, and response generation.
Strong knowledge of Vector Databases and embedding technologies , with experience optimizing retrieval and relevance.
Hands-on experience with LangGraph, CrewAI, Temporal , or similar agent orchestration frameworks.
Experience integrating AI agents with enterprise applications using REST APIs, MCP, and A2A protocols .
Good understanding of Azure OpenAI, AWS Bedrock, or other enterprise LLM platforms .
Experience with Docker, Kubernetes, CI/CD, Azure DevOps, Argo CD, and GitOps practices.
Strong understanding of AI security, OAuth2/JWT, Responsible AI guardrails, governance, and enterprise application security .
Experience with AI evaluation, observability, monitoring, logging, and tracing , using tools such as LangSmith, OpenTelemetry, or Elasticsearch.
Good-to-have experience with Semantic Kernel, AWS Bedrock AgentCore , and enterprise AI governance.
Strong analytical, problem-solving, stakeholder management, collaboration, and communication skills.

RESPONSIBILITIES

Design, develop, and deploy enterprise-grade AI agents, Agentic AI, and conversational AI solutions using Python and FastAPI .
Build and manage multi-agent systems , including workflow orchestration, reasoning, memory, tool integration, and agent coordination.
Design and implement scalable RAG-based AI solutions using vector databases, embeddings, and advanced prompt engineering techniques.
Develop and optimize LLM prompts, retrieval strategies, agent workflows, and AI responses for accuracy, reliability, and business relevance.
Integrate AI agents with enterprise applications using REST APIs, MCP, and A2A protocols .
Implement secure and scalable cloud-native AI applications with OAuth2/JWT, Key Vault, Responsible AI guardrails, and governance controls .
Build and maintain CI/CD pipelines using Azure DevOps, Docker, Kubernetes, Argo CD, and GitOps practices.
Establish automated testing, observability, monitoring, logging, tracing, and AI evaluation using tools such as LangSmith, OpenTelemetry, and Elasticsearch.
Optimize AI solutions for performance, scalability, reliability, latency, cost efficiency, and production readiness .
Collaborate with business, platform, security, cloud, and engineering teams to deliver enterprise-grade AI solutions .
Evaluate and adopt emerging LLMs, Agentic AI frameworks, AI engineering tools, and industry best practices .
Provide technical leadership and mentor engineering teams on GenAI, LLM applications, RAG, agent architecture, and production AI engineering .

Bachelor’s or master’s degree in computer science, Information Technology, or a related field.