Senior AI Application Engineer

Whitefield Careers; Dadri; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Whitefield Careers is seeking a hands-on AI Application Engineer to design and ship GenAI/SLM applications for air-gapped and on-prem environments. You will implement RAG pipelines, agents, and fine-tuned models, exposing APIs and UI for enterprise integration.

Responsibilities include productionizing RAG workflows, offline inference, vector stores, and Docker/Kubernetes deployment across cloud and on‑prem targets.

Qualifications

• 5+ years of professional software engineering.
• 2+ years building GenAI/ML applications in production.
• Strong Python skills with experience in LLM/SLM tooling.
• Hands-on with LangChain, LlamaIndex or similar frameworks.
• Experience with RAG, prompting, fine-tuning, or quantization.

Responsibilities

• Build and productionize RAG pipelines and agentic workflows.
• Fine-tune, quantize, and package SLMs for offline environments; benchmark performance.
• Implement local/offline inference serving and vector store integrations for air-gapped deployments.
• Write clean, testable Python code for APIs, data pipelines, and integration layers.
• Containerize and deploy apps across cloud and on-prem targets.
• Build evaluation harnesses, guardrails, and monitoring for model outputs.
• Work in sprints, manage blockers, and demo working software each sprint.

Skills

Python

LangChain

LlamaIndex

RAG pipelines

LLMs/SLMs

Docker/Kubernetes

Cloud (AWS/Azure/GCP)

REST/API design

CI/CD

Code review & specs

Agent-based development

Tools

FAISS

Milvus

Weaviate

Qdrant

Job description

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.

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.