Description

Job Description:

• Bachelor’s degree in related field.
• AI Native Engineering.
• 7–12+ years of overall software engineering experience.
• 3–5+ years in AI/ML engineering with at least 2 years in Generative AI and Agentic AI in production.
• Java — Enterprise applications, Spring Boot, microservices, AI SDK integration.
• C++ — Systems-level development, AI inference engine integration (ONNX, TensorRT, llama.cpp).
• Python — LLM orchestration (LangChain, LangGraph, AutoGen), data pipelines, FastAPI.
• Production experience with end-to-end AI-powered business applications.
• Proven expertise in Agentic AI architectures — tool-use, memory management, multi-agent orchestration, and human-in-the-loop workflows.
• Hands-on across AI SDLC use cases — AI-driven code review, automated test generation, intelligent QA pipelines, and AI-powered requirements analysis.
• Proficiency with LLM APIs (OpenAI, Anthropic, Azure OpenAI, or open-source equivalents), RAG pipelines, vector databases, and embedding strategies.
• Solid experience in traditional application security — OWASP Top 10, secure coding, SAST/DAST, API security, and threat modeling.
• Hands-on with AI-specific security risks — prompt injection, RAG poisoning, model data leakage, and adversarial inputs.
• Familiarity with OWASP LLM Top 10, NIST AI RMF, MITRE ATLAS.
• Excellent communication and presentation skills, with the ability to explain complex technical concepts to both technical and non-technical audiences.
• Lead Automation Transformation & Provide Visibility to Business Segment & Leadership.

Nice to Have:

• AI integration with enterprise platforms (ERP, CRM, DevSecOps toolchains).
• LLM observability and evaluation (RAGAS, DeepEval, Promptfoo).
• AI governance, responsible AI, and explainability in enterprise contexts.
• Open-source contributions or published technical content

Key Skills

Education

Any Graduate