AI Solution Architect

Location- Delhi(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.

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 setting

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

📌 AI Solution architect (Delhi)
🏢 iXceed solutions
📍 Delhi