Staff Engineer, Generative AI Engineer

REQUIREMENTS:

• Total experience: 5.5+ years, with strong recent experience in AI/GenAI engineering.
• 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. Qualifications

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