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 professional 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.

Required Skills:

python GenAI LLM Application Development LLM RAG Pipelines RAG AI Agents Agentic Workflows LangChain LangGraph LlamaIndex SLM Prompt Engineering Fine-tuning LoRA QLoRA Model Quantization Vector Databases FAISS Milvus Weaviate Qdrant Embedding REST API Development FastApi docker Kubernetes Cloud vLLM Ollama On-prem deployment experience Claude Code MCP Code review of AI bigdata git