About the role
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