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Mid-level LLM full-stack role with popular skills and metro location increases candidate competition.
Deep LLM, RAG and fine-tuning expertise makes cross-industry transfers difficult.
Explicit 4-6 years plus mandatory LLM, RAG, fine-tuning, and cloud deployment skills raise strictness.
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Design, build, and deploy end-to-end AI agents, RAG systems, fine-tuned small language models, and full-stack AI applications including frontend (React/Next.js) and backend (Python/Node.js).
Own deployment, scaling, and observability of AI solutions on cloud platforms (AWS, GCP) using container orchestration (Docker, Kubernetes) and infrastructure as code (Terraform).
Apply AI-augmented development techniques using AI coding assistants, ensuring disciplined management of context, token usage, and AI-generated code evaluation.
4–6 years of total experience; 2+ years hands-on production experience with LLMs (OpenAI, Anthropic Claude, Gemini, or open-source).
Onsite location: Ahmedabad mandatory.
Strong production experience with RAG pipelines, AI agents (LangChain, LangGraph), fine-tuning small/open-source LLMs using LoRA, QLoRA, PEFT.
Full-stack engineering skills: Python (FastAPI/Flask) and/or Node.js backend, React/Next.js frontend, cloud deployment experience (AWS/GCP).
Experienced in building and optimizing scalable and secure AI/GenAI products with end-to-end ownership from model fine-tuning to full-stack deployment.
Proficient in advanced AI workflows including prompt engineering, multi-agent design, evaluation-driven engineering, and handling non-functional requirements like latency and cost optimization.
Comfortable operating in a highly technical, cloud-native environment applying AI coding assistants daily, with strong discipline in token/context management and code quality assurance.