Job Description :

Role : AI Solution Architect

Location : Bangalore (Hybrid) India

Experience : 6 to 10 Years

Role Overview :

We are building the next generation of AI-native products and are 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 AI systems production-ready at scale. You will work at the intersection of AI research and engineering, 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 define 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 :

1. Planner-executor patterns

2. Tool-using agents

3. Workflow automation agents

4. Dynamic routing and orchestration

- Define system architecture for :

1. RAG pipelines

2. Memory systems (short-term, long-term, vector-based)

3. Context management

4. Prompt orchestration

5. Stateful workflows

Pipeline Engineering :

- Build and optimize AI pipelines for :

1. Latency optimization

2. Cost optimization (token efficiency)

3. Scalability

4. Reliability

- Design integration patterns with enterprise systems, including 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 AI systems.

- Contribute to platform-level initiatives, including tooling, SDKs, and reusable components.

Required Skills & Experience :

Technical Experience :

- 6 to 10 years of experience in backend engineering or distributed systems.

- 3 to 4 years of hands-on, production-grade experience with Generative AI or LLM-based systems.

- Demonstrable experience in shipping AI systems at scale, beyond prototypes.

Generative AI & LLM Skills :

- Strong understanding of LLM architectures, capabilities, and limitations.

- Hands-on experience with agentic orchestration frameworks, such as :

1. LangChain

2. LangGraph

3. AutoGen

4. CrewAI

5. Comparable tools

- Experience with RAG architectures, embedding models, and vector databases.

- Strong prompt engineering and context design skills.

Architecture & Systems :

- Expertise in :

1. System design

2. Scalability & performance optimization

3. Fault tolerance

4. Cost optimization

- Experience designing backend systems and APIs.

- Understanding of asynchronous 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 scalable AI architectures.

- Comfortable operating as a Senior Individual Contributor (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.) alongside proprietary APIs.