Lead AI Architect: LLM Systems, RAG and Agentic Workflows

Vibehackers

United States

On-site

USD 180,000 - 260,000

Full time

9 days ago

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Job summary

MSys Tech India Pvt Ltd in Chennai seeks an AI Software Architect to lead design and delivery of production-grade LLM-powered systems and agentic workflows. You will architect scalable RAG solutions, tool-calling, multi-agent orchestration, and AI observability, coordinating with backend, web, and mobile teams.

The role requires 10+ years in software engineering with 5+ years in LLM-based AI, strong Python, and experience with Docker/Kubernetes and GCP.

Qualifications

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.

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.