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
