Senior AI Application Engineer Location: Noida Type: Full-Time, Permanent Experience: 5+ years Role Overview We are looking for a hands-on AI Application Engineer to build and ship the GenAI/SLM applications designed by our Technical Program Leads — including for air-gapped and on-prem environments. RAG pipelines, agents, fine-tuned models, and the APIs/UI that expose them. Key Responsibilities Build and productionize RAG pipelines, agentic workflows, and LLM/SLM-backed features from architecture specs handed off by the Technical Program Lead. Fine-tune, quantize, and package SLMs for constrained/offline environments; benchmark accuracy, latency, and cost against alternatives. Implement local/offline inference serving (vLLM, Ollama) and vector store integrations (FAISS, Milvus, Weaviate, Qdrant) for air-gapped deployments. Write clean, testable, well-documented Python — APIs, data pipelines, and integration layers connecting LLM components to enterprise systems. Containerize and deploy applications (Docker/Kubernetes) across cloud (AWS/Azure/GCP) and on-prem targets. Build evaluation harnesses, guardrails, and monitoring/logging for model outputs in line with the governance framework set by the Technical Program Lead. Work sprint-to-sprint in JIRA — pick up stories, raise blockers early, keep the board current, and demo working software each sprint. Required Skills & Experience: 5+ years qualified software engineering; 2+ years building GenAI/ML applications in production. Strong Python; hands-on with LangChain, LlamaIndex, or similar frameworks. Practical experience with LLMs/SLMs — prompting, RAG, fine-tuning (LoRA/QLoRA), or model quantization. Working knowledge of vector databases and embedding pipelines. Comfortable with Docker/Kubernetes and at least one major cloud (AWS/Azure/GCP).



Expert with Claude-driven development — uses Claude Code / Claude-based agents daily as part of the build workflow; comfortable authoring or using custom Skills/MCP tools to speed up delivery. Reviewer, not just implementer: most code is agent-generated first; your core skill is writing tight specs, critically reviewing agent output line-by-line, catching bugs/edge cases/security issues, and deciding when to trust vs. override the agent — rather than manually writing everything from scratch. Solid understanding of REST/API design, git workflows, and CI/CD basics. Behavioural Expectations Execution-focused: comfortable taking a spec from the Technical Program Lead and running with it with minimal hand-holding — but "execution”; here means directing and reviewing agentic output, not manual coding for its own sake. Fluent in Agile/Scrum — active participant in ceremonies, disciplined about JIRA hygiene and sprint commitments. Clear communicator — flags risks/blockers early, documents decisions, and can explain technical trade-offs to the Technical Program Lead and, when needed, the client. Mentors junior AI Application Engineers — reviews their code/PRs, helps them write better specs for AI coding agents, and brings them up to speed on RAG/SLM patterns and air-gapped deployment practices. Self-driven and self-governed, per company's high-ownership hybrid culture. Mentor juniors Preferred: background in an IT/consulting services company. Good to Have Exposure to Big Data tooling (Spark/Hive/Hadoop) or Graph Analytics. Experience in a regulated or air-gapped delivery environment (defense, government, BFSI). Familiarity with AI governance/evaluation frameworks (guardrails, red-teaming, model cards). Contribution to open source projects, academic papers published, filled patents.

📌 Senior AI Application Engineer (Noida)
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