About the job
Develop and integrate enterprise AI solutions, including GenAI apps, RAG pipelines, AI agents, copilots, automation workflows, and reusable AI platform components. This role involves building GenAI apps, RAG components, prompt flows, data connectors, APIs, and AI agents, while designing and developing responsive user interfaces using React, Next.js, TypeScript, HTML5, CSS3, and modern UI component libraries (Material UI, Tailwind CSS or similar).
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Key Responsibilities
- Implement vector indexes, embeddings, document processing, and knowledge grounding using Azure AI services.
- Create PoCs, MVPs, and reusable accelerators for client demonstrations and delivery mobilization.
- Support testing, evaluation, groundedness checks, performance tuning, monitoring, and cost optimization.
- Document designs, deployment steps, and reusable implementation patterns.
- Collaborate with consultants, product owners, architects, UX designers, and governance specialists.
Experience And Skills Required
- 5-8 years in software, data, AI, cloud development, or automation.
- Hands-on work building GenAI, Agentic Harness, RAG, chatbot, AI/ML, or enterprise integration solutions.
- Good Python and frontend capability plus APIs, SDKs, and source control.
- Experience in agile teams and ability to communicate technical trade-offs clearly.
- Proficiency in Python, FastAPI or similar backend and frontend frameworks.
- Strong experience in Python and modern frontend development using React or Next.js.
- Fundamentals of Azure OpenAI, Azure AI Foundry, Azure AI Search, and Azure ML.
- Knowledge of RAG pipelines, embeddings, vector search, prompt engineering, and evaluation.
- Familiarity with agent frameworks, orchestration, tool calling, and workflow automation.
- Experience with APIs, databases, message queues, files, and enterprise connectors.
- Knowledge of Git, CI/CD, containers, configuration, and automated testing.
- Understanding of AI quality metrics: test datasets, regression checks, hallucination checks, groundedness, latency, and cost.
- Responsible AI implementation: content filters, guardrails, logging, and human review.
