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Remote role, mid-level requirement, and broad GenAI skillset increase competition.
Specialized Generative AI engineering skills and enterprise agent experience limit cross-industry transferability.
Many mandatory GenAI, infra, and security skills plus explicit 5.5+ years make screening strict.
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Design, develop, and deploy enterprise-grade AI agents and conversational AI solutions using Python and FastAPI.
Build and manage multi-agent systems including workflow orchestration, reasoning, memory, and tool integration with focus on scalable RAG-based AI solutions.
Implement secure, scalable, cloud-native AI applications with CI/CD pipelines, monitoring, and governance controls, collaborating with cross-functional teams to deliver production-ready AI solutions.
Total experience: Minimum 5.5 years with strong recent experience in AI/Generative AI engineering.
Strong hands-on experience with Python, FastAPI, vector databases, embeddings, LLMs, prompt engineering, RAG pipelines, and agent orchestration frameworks (e.g., LangGraph, CrewAI, Temporal).
Experience with cloud enterprise LLM platforms (Azure OpenAI, AWS Bedrock), containerization (Docker, Kubernetes), CI/CD (Azure DevOps, Argo CD), and security practices including OAuth2/JWT and Responsible AI governance.
Bachelor’s or master’s degree in Computer Science, Information Technology, or related field.
Experienced in designing and optimizing scalable AI agentic architectures with strong expertise in RAG pipelines and multi-agent orchestration.
Proficient in integrating enterprise AI solutions with REST APIs, MCP, and A2A protocols, emphasizing production-grade security and governance.
Strong technical leadership experience mentoring teams on Generative AI, LLM applications, and AI engineering best practices in enterprise environments.