AI Solution Architect

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