AI Solution Architect

Wfnen; Bengaluru; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

Wfnen in Bengaluru is searching for an AI Solution Architect to design and own scalable architectures for cutting-edge AI products. In this pivotal role, you will be responsible for developing LLM-powered platforms and orchestration layers, ensuring they are production-ready and reliable.

The ideal candidate will have 6–10 years of backend engineering experience, with a solid foundation in Generative AI systems. If you're driven by architectural complexity in AI-native environments, this opportunity is for you.

Qualifications

• 6–10 years in backend engineering or distributed systems.
• 3–4 years of hands-on, production-grade experience in Generative AI or LLM-based systems.
• Experience shipping AI systems at scale is required.

Responsibilities

• Design scalable architectures for agentic AI systems.
• Architect multi-agent systems with dynamic orchestration.
• Build AI pipelines for cost optimization and reliability.
• Define integration patterns with enterprise systems.
• Establish observability and evaluation frameworks.

Skills

Generative AI Systems

LLM Architectures

Backend Engineering

Orchestration Frameworks

Prompt Engineering

System Scalability

APIs Design

Job description

Bangalore North, India | Posted on 06/01/2026

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 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

• 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.
• 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.
• 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.
• 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

• Understanding of AI governance, security, and compliance.
• Exposure to open‑source LLM ecosystems (Llama, Mistral, etc.) in addition to proprietary APIs.