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 :

1. 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.

2. 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.

3. 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.

4. 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.