AI Solution Architect

Job Description AI Solution Architect Location: India Remote / Hybrid Experience 6–10 years of total experience in backend or distributed systems engineering, with at least 3–4 years of hands-on, production-focused experience in Generative AI or LLM-based systems. Role Overview We are building the next generation of AI-native products, and we're looking for an AI Solution Architect to be a core part of that foundation. This is not a consulting or advisory role. You will own architecture end-to-end — designing agentic systems, LLM-powered platforms, and the orchestration layers that make them production-ready at scale. You'll work at the intersection of cutting-edge AI research and real-world engineering constraints, shaping how we build and evolve our AI platform. If you're excited by the complexity of multi-agent systems, the challenge of making LLMs reliable and cost-efficient in production, and the opportunity to set architectural standards in a fast-moving AI-native environment — this role is for you. Key Responsibilities System Architecture · Design and own scalable architectures for agentic AI systems and LLM-powered platforms · Architect multi-agent systems including planner-executor patterns, tool-using agents, workflow automation agents, and dynamic routing and orchestration · Define system design for RAG pipelines, memory systems (short-term, long-term, vector-based), context management, prompt orchestration, and stateful workflows Pipeline Engineering · Build and optimize AI pipelines for latency, cost (token optimization), scalability, and reliability · Design integration patterns with enterprise systems — APIs, databases, and downstream services Reliability & Governance · Establish observability, tracing, and evaluation frameworks for AI systems · Define guardrails, safety layers, and failure handling mechanisms · Drive best practices in prompt engineering, system design, and AI architecture Collaboration · Work closely with engineering, product, and research teams to translate use cases into production-grade systems · Contribute to platform-level thinking — tooling, SDKs, reusable components Required Skills & Experience Technical Experience · 6–10 years in backend engineering or distributed systems · 3–4 years of hands-on, production-grade experience with Generative AI or LLM-based systems · Demonstrable experience shipping AI systems at scale — not just prototypes Generative AI & LLM Skills · Strong understanding of LLM architectures, capabilities, and limitations · Hands-on experience with agentic orchestration frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or comparable tools · Experience with RAG architectures, embedding models, and vector databases · Strong prompt engineering and context design skills Architecture & Systems · Expertise in system design, scalability, performance optimization, fault tolerance, and cost optimization · Experience designing backend systems and APIs · Understanding of async workflows and event-driven architectures · Familiarity with cloud platforms (AWS, Azure, or GCP) · Exposure to MLOps / LLMOps workflows · Familiarity with observability and tracing tools Soft Skills · Ability to translate ambiguous business problems into concrete, scalable AI architectures · Comfort operating as a senior IC in a fast-moving, AI-native environment Preferred Qualifications · Experience building AI platforms, internal tooling, or developer-facing SDKs · Understanding of AI governance, security, and compliance · Exposure to open-source LLM ecosystems (Llama, Mistral, etc.) in addition to proprietary APIs