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Vacancy description

Senior AI Developer - InfraSec Automation (L2) in Bombay, भारत

Theomnihire

  • ContractFixed-term or freelance

Bombay, भारत

Job Title: Senior AI Developer – InfraSec Automation (L2)

Location: Reliance Corporate Park (RCP), Navi Mumbai

Working Hours: 9:00 AM – 6:00 PM

Mode of Interview: Face-to-Face or MS Teams

Headcount: 2 Positions

Position Summary

The Senior AI Developer – InfraSec Automation (L2) is a hands-on technical role focused on designing, building, and deploying production-grade, LLM-powered features and AI tools for infrastructure security workflows. Working within the Cyber Security Division, you will bridge AI engineering and security operations by developing end-to-end RAG pipelines, agentic workflows, automated remediation assistants, and evaluation harnesses. You will write production-quality Python and Java code, implement security controls around AI services, and integrate automated AI capabilities natively into security platforms, SIEMs, and cloud infrastructure.

Requirements

Key Responsibilities

  • AI Feature Development: Design, build, and ship LLM-powered security assistants that support vulnerability summarization, log triage, remediation recommendations, policy reviews, and natural-language queries over security data.

  • Prompt Engineering & Schemas: Develop prompt templates, system prompts, and structured-output schemas (JSON schema, function calling), continuously iterating via offline and online evaluations.

  • End-to-End RAG Pipelines: Implement Retrieval-Augmented Generation (RAG) pipelines including chunking strategies, embeddings management, vector store integration, retrieval tuning, and grounding.

  • Microservices & API Development: Build and operate Python-based AI microservices and APIs (using FastAPI/Flask) that wrap LLM providers (Anthropic, OpenAI, Azure OpenAI, Vertex AI) and open-source models, alongside supporting backend services in Java where required.

  • Lightweight UIs & Dashboards: Develop lightweight frontend components (HTML, CSS, JavaScript) for internal security tools, AI assistants, and operational dashboards.

  • LLM Evaluation & Quality Control: Implement evaluation harnesses, golden datasets, and regression suites to measure LLM performance, tracking accuracy, hallucination rates, latency, and API cost.

  • AI Security & Guardrails: Apply responsible-AI controls including prompt-injection mitigations, PII redaction, output filtering, rate limiting, audit logging, and access controls.

  • Ecosystem Integrations: Connect AI services with security scanners, ticketing platforms, SIEMs, and monitoring stacks to deliver actionable, automated security workflows.

  • Automation & MLOps: Automate data preparation, embedding refreshes, eval runs, and health checks using Python, Shell scripts, Git, Docker, and CI/CD pipelines.

  • Cross-Functional Collaboration: Partner with security, DevOps, infrastructure, and engineering teams to translate complex security requirements into scalable AI solutions.

Candidate Profile & Qualifications

  • Experience: 4–6 years of core software engineering and AI development experience.

  • Education: https://jobeax.com/link/2DENLGgQM7whDhaE or https://jobeax.com/link/s1WvHFV9ITSwCkyS in Computer Science, Information Technology, AI/ML, or a related field.

  • Core Technical Skills:

    • Python & Backend: Strong Python (FastAPI/Flask, async patterns, packaging, testing); working knowledge of Java for backend REST APIs.

    • LLM Frameworks & APIs: Hands-on experience with LLM provider APIs (Anthropic, OpenAI, Azure OpenAI, Vertex AI) and frameworks (LangChain, LlamaIndex, LangGraph, or Haystack).

    • Vector Databases & RAG: Practical experience with vector stores (pgvector, Pinecone, Weaviate, Chroma, FAISS, Azure AI Search, or Vertex AI Vector Search).

    • LLM Evals & Tooling: Experience building evaluation sets using frameworks like RAGAS, DeepEval, Promptfoo, or LangSmith.

    • Infrastructure & Web: Linux/Shell scripting, Docker, Git, CI/CD, basic HTML/CSS/JS, and experience with at least one major cloud platform (Azure, GCP, or AWS).

  • Certifications (Any one required):

    • CISSP, CCSP, AZ-500, Azure AI Engineer Associate (AI-102), AWS Certified Security – Specialty, or Google Professional Cloud Security Engineer.

    • (Desirable: CISM, CEH, OSCP, Google PMLE, AWS ML Specialty, CKA, Terraform Associate, or SC-100).

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