Ai Software Architect Llm Agentic Systems Msys Tech India Pvt Ltd Chennai
Directly building agentic LLM systems, RAG pipelines, and observability — so it's all about architecting AI tooling and production LLM workflows.
About the Role
Lead architecture and delivery of production-grade LLM-powered systems and agentic workflows, designing scalable RAG, observability, evaluation, and orchestration for real-world AI products. Provide technical leadership across engineering teams to build reliable, cost-effective AI services integrated with backend, web, and mobile applications.
Job Description
Role
The AI Software Architect / Senior AI Engineer will design, build, and operate production-grade LLM-powered systems and agentic workflows. The role focuses on architecting scalable Retrieval-Augmented Generation (RAG) solutions, tool-calling and multi-agent orchestration, evaluation frameworks, and AI observability while integrating AI services with backend, web, and mobile applications.
Key Responsibilities
- Own architecture and technical decisions for AI-powered services and their integration with backend, mobile, and web apps.
- Design, build, and maintain production-grade LLM applications using modern AI frameworks and orchestration platforms.
- Architect agentic workflows involving tool use, function calling, planning, memory management, and multi-agent systems.
- Establish evaluation frameworks to measure AI quality, reliability, latency, cost, and business impact.
- Implement AI observability and monitoring with platforms such as Langfuse, LangSmith, OpenTelemetry, and related tooling.
- Design and implement RAG architectures using vector databases, embeddings, and document pipelines.
- Lead technical design reviews and drive architectural decisions across AI services, backend systems, data pipelines, and cloud infrastructure.
- Define AI engineering standards, best practices, and governance; document architectures, workflows, and operational procedures.
- Provide technical leadership and mentorship to engineering teams.
- Optimize prompts, retrieval strategies, model selection, performance, and cost efficiency.
- Leverage cloud platforms (e.g., GCP) and cloud-native tooling to deploy and scale AI services.
Requirements
- Master’s degree in Computer Science, AI, ML, Software Engineering, or related field (preferred).
- 10+ years of software engineering experience with at least 5 years focused on LLM-based solutions and generative AI systems.
- Demonstrated experience designing and deploying complex production-grade AI applications using LLMs.
- Extensive experience with AI orchestration frameworks (examples: LangGraph, LangChain, LlamaIndex, CrewAI).
- Hands-on experience implementing AI observability and evaluation frameworks (examples: Langfuse, LangSmith, OpenTelemetry).
- Proven expertise with RAG architectures and vector database technologies.
- Strong Python development experience and solid software engineering fundamentals.
- Experience deploying AI solutions using Docker, Kubernetes, and cloud platforms (GCP mentioned).
- Familiarity with structured and unstructured data processing pipelines and modern databases (PostgreSQL, vector DBs, document stores).
- Strong communication, leadership, and mentoring skills; ability to collaborate with cross-functional teams.
Preferred
- Experience building agentic systems (tool use, planning, memory, multi-agent orchestration).
- Experience with model evaluation, benchmarking, fine-tuning, synthetic data generation, and model optimization.
- Experience with multiple commercial and open-source models (OpenAI, Anthropic, Gemini, Llama, Mistral).
- Experience supporting AI products in regulated, privacy-sensitive, or high-availability environments.
- Familiarity with DevOps practices, CI/CD, and Agile development.
Not included
- Boilerplate company marketing, hiring process steps, and application instructions have been omitted.