AI Engineer

About the Role:

We are seeking an AI Native Engineer with proven hands-on experience building enterprise business applications and agentic AI solutions in production. The role requires strong technical depth across AI-augmented software development and application security. You will work alongside an experienced in-house AI expert and are expected to contribute at a peer level.

Key Responsibilities:

• Design and deliver production-grade business applications with AI natively integrated into the architecture.
• Build and operate Agentic AI systems — multi-step, autonomous pipelines using frameworks such as LangGraph, AutoGen, CrewAI, or equivalent.
• Drive AI SDLC practices across the development lifecycle — AI-assisted code generation, review, testing, documentation, and CI/CD augmentation.
• Apply application security expertise in both traditional and AI-specific contexts.
• Evaluate and integrate emerging AI tools and frameworks with sound engineering judgment.
  
  

Required Qualifications:

• AI Native Engineering
• 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.
  
  

Application Security:

• 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.
  
  

Programming Languages (Minimum one, ideally two):

• 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.
  
  

Experience:

• 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.
  
  

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.