AI Solution Architect
Job Location:
Monthly Salary:
Experience Required:
Posted on:
Vacancies:
Job Summary
AI Solution Architect
Location: India Remote / Hybrid
Experience 610 years of total experience in backend or distributed systems engineering with at least 34 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 were 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. Youll work at the intersection of cutting-edge AI research and real-world engineering constraints shaping how we build and evolve our AI platform.
If youre 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
610 years in backend engineering or distributed systems
34 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
Required Skills:
AI Solution Architect Location: India Remote / Hybrid Experience 610 years of total experience in backend or distributed systems engineering with at least 34 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 were 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. Youll work at the intersection of cutting-edge AI research and real-world engineering constraints shaping how we build and evolve our AI platform. If youre 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 610 years in backend engineering or distributed systems 34 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
Required Education:
MBA
