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Specialized LLM plus full-stack mid-level role reduces broad applicant pool, yielding medium competition.
Deep LLM production and fine-tuning experience is highly domain-specific, making cross-industry fit limited.
Mandated production LLM experience, RAG, fine-tuning, cloud and full-stack skills create high screening strictness.
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Design, build, and deploy production-grade AI agents, RAG systems, and fine-tuned small language models with end-to-end full-stack delivery including frontend, backend, and cloud deployment.
Own deployment, scaling, and observability on AWS and GCP using Docker, Kubernetes, Terraform, and CI/CD pipelines.
Implement disciplined AI-augmented development practices including evaluation-driven workflows and secure, cost-efficient non-functional requirements management (latency, security, reliability).
4 to 6 years of total experience required, with at least 2 years of hands-on production work using LLMs (OpenAI, Anthropic, Gemini, or open-source).
Must have production experience with LangChain, LangGraph (or equivalent) in building AI agents and strong RAG pipeline expertise including vector databases like Pinecone, Weaviate, Qdrant, or pgvector.
Proficient in full-stack development with React/Next.js and Python (FastAPI/Flask) and/or Node.js (TypeScript), plus experience deploying on AWS (Bedrock, SageMaker, Lambda, ECS/EKS) and GCP (Vertex AI, Cloud Run, GKE).
Onsite work location: Ahmedabad, India.
Experienced full-stack AI engineer capable of end-to-end ownership from frontend UI to backend infrastructure and cloud deployment with production AI systems.
Deep expertise in AI agent orchestration, RAG architectures, fine-tuning small LLMs, and operational discipline around AI coding assistants in daily workflows.
Comfortable managing complex non-functional engineering challenges such as latency optimization, cost efficiency, prompt injection defense, and advanced observability in AI product environments.