AI Engineer

• 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 client-facing, presales-adjacent capacity when needed

At Strada, our values guide everything we do:

• Anticipate Customer Needs – We stay ahead of trends so our customers can grow and succeed.

• Own the Outcome – We take responsibility for delivering excellence and ensuring things get done right.

• Challenge Ourselves to Work Smarter – We move faster than the world around us to drive change and accomplish more.

• Empower Each Other to Solve Problems – We tackle challenges head on, ask tough questions, and collaborate to find the best solutions.

• Care About Our Work – We understand that what we do impacts millions, and we have a responsibility to get it right.