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Specialized AI skills but popular mid-level title increases applicant density.
Core LLM, RAG, and backend engineering skills are transferable, though enterprise domain experience adds moderate bias.
Numerous mandatory AI, RAG, backend, and deployment skills create moderately strict technical filters.
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Design, build, and deploy AI-powered applications leveraging LLMs, RAG systems, and agentic workflows across internal and customer-facing use cases.
Develop backend services, APIs, and integration layers to embed AI capabilities in enterprise applications, ensuring production readiness and scalability.
Implement monitoring, evaluation, and guardrails for deployed AI systems, optimizing performance, latency, accuracy, and cost efficiency.
Strong proficiency in Python programming and software engineering fundamentals.
Experience building backend services and APIs using frameworks like FastAPI or Flask.
Hands-on experience with LLM application development, prompt engineering, and RAG systems including retrieval strategies and evaluation.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced operating in fast-paced environments with evolving requirements, emphasizing reliability, accuracy, and disciplined engineering.
Proven ability to translate business requirements into practical, scalable AI solutions with measurable impact.
Familiarity with AI orchestration frameworks (e.g., LangChain), vector databases, and enterprise integration patterns to support complex AI workflows.