AI Engineer

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Role: AI Engineer & Architect Role Summary We are looking for an AI Engineer and Solution Architect. This is a working lead role, not a purely managerial one — it requires someone who can design and build production LLM/RAG and agentic systems personally, while also setting the technical direction, standards, and delivery discipline for the team building them.

The ideal candidate has a strong core software engineering foundation (full-stack / .NET or equivalent enterprise stack) and has since specialized into applied AI. Key Responsibilities

• Define the team’s technical standards, reusable patterns, and reference architecture for GenAI/Agentic AI delivery across engagements.
• Own and manage the AI budget — tooling and LLM/API spend, infrastructure costs, and licensing — and justify spend against delivery outcomes.
• Architect and build end-to-end GenAI and Agentic AI solutions — RAG pipelines, LLM orchestration, multi agent workflows — from design through production deployment.
• Own solution architecture across the full stack: backend services, APIs/microservices, data layer, and cloud infrastructure, ensuring AI components integrate cleanly with existing enterprise systems.
• Select and justify the technical stack — LLM providers, vector databases, orchestration frameworks — based on the problem, not on default habit, and codify these choices as team guidelines.
• Produce architecture artifacts (HLD/LLD, sequence diagrams, integration specs) that the team builds against, and review their designs for architectural soundness.
• Build in evaluation, guardrails, and monitoring for AI systems — hallucination checks, retrieval quality metrics, human-in-the-loop escalation paths — as a standard the team enforces, not a one-off step.
• Support presales and solutioning — translating client requirements into a credible technical approach and effort estimate, and representing the team in client-facing technical discussions.
• Mentor and technically lead the team, growing its GenAI capability and setting the bar for what “production ready” means. Required Skills — AI / GenAI
• Hands-on experience with LLMs (OpenAI, Gemini, or equivalent) and prompt engineering for production use cases.
• RAG architecture — chunking, embeddings, hybrid retrieval, reranking — and the trade-offs between them.
• Agentic AI patterns: tool-calling, planner/executor designs, multi-agent orchestration (LangChain / LangGraph or equivalent).
• Vector databases (FAISS, Pinecone, pgvector, or equivalent) and semantic search implementation.
• Working ML/DL fundamentals — comfortable evaluating when a classical model or fine-tuning approach is the better answer than an LLM call.
• Python as the primary language for AI/ML work. Required Skills — Engineering & Architecture
• Strong core software engineering background, with proficiency in .NET/C# and/or Java (Spring/Spring Boot) at an architecture level, not just scripting.
• REST API and microservices design; comfortable defining service boundaries and contracts.
• Cloud platform experience (Azure, AWS, or GCP) — deployment, scaling, and security basics for production services.
• SQL and NoSQL database design; understands when each is the right fit.
• Containerization and CI/CD (Docker and a standard pipeline tool).
• Demonstrated solution architecture experience — has owned HLD/LLD for at least one non-trivial system, not just contributed to one.

Experience & Profile

• 12–17+ years overall in software engineering, with a clear architecture or technical-lead track record.
• At least 2–3 years of recent, hands-on GenAI/Agentic AI project experience — not certifications alone.
• Comfortable operating in a…