Senior MLOps Technical Lead

• The Senior MLOps Technical Lead will be responsible for building and scaling production-grade AI/LLM systems, including agentic workflows and distributed backend services.
• The role focuses on designing, deploying, and operating AI systems in production, ensuring scalability, reliability, cost efficiency, and real-world performance of AI-driven applications.

Requirement/Must Have:

• Minimum 3+ years of experience building production backend or distributed systems.
• Proven experience shipping AI/LLM features to real production users at scale.
• Strong experience building AI agents, tools, skills, or MCP (Model Context Protocol) integrations.
• Strong Python development experience for backend systems.
• Working knowledge of Go, TypeScript, or Rust.
• Strong experience with cloud platforms (AWS, Google Cloud Platform, or Azure).
• Hands-on experience with Docker and Kubernetes for deployment and scaling.
• Strong understanding of LLM behavior including token limits, rate limits, and API constraints.
• Experience evaluating LLM outputs, performance, and system quality.
• Strong hands-on engineering experience (coding, debugging, deployment).

Experience:

• Experience building and deploying production-grade AI/LLM applications.
• Experience designing agentic systems and multi-step AI workflows.
• Experience integrating LLM APIs and handling failure modes in production systems.
• Experience with cloud infrastructure optimization including cost and performance tuning.
• Experience building scalable backend services and distributed systems.
• Experience working with AI orchestration frameworks such as LangGraph or CrewAI.
• Experience implementing guardrails, fallbacks, and reliability patterns for AI systems.
• Experience with streaming inference and asynchronous agent execution.
• Experience in ML observability tools such as Langfuse, Arize, or Weights & Biases.
• Experience with retrieval systems such as vector or hybrid search (supporting role).

Responsibilities:

• Design, build, and deploy production AI/LLM-powered systems and agentic workflows.
• Develop scalable backend services and distributed systems for AI applications.
• Build and maintain MCP integrations and tool-use frameworks.
• Deploy AI services on Kubernetes and manage scaling, reliability, and performance.
• Optimize system cost, latency, and compute usage across cloud infrastructure.
• Evaluate and improve LLM output quality, reliability, and performance.
• Implement monitoring, observability, and evaluation frameworks for AI systems.
• Build fallback mechanisms, guardrails, and error-handling strategies for AI systems.
• Debug production issues and continuously improve system stability.
• Collaborate with product and engineering teams to deliver production AI features.

Should Have:

• Experience building multi-step agentic workflows with tool use and function calling.
• Experience with orchestration frameworks like LangGraph or CrewAI.
• Experience with AI system observability tools such as Langfuse, Arize, or W&B.
• Experience implementing caching, batching, and prompt optimization strategies.
• Experience with streaming inference and async execution systems.

Skills:

• Strong backend and distributed systems engineering skills.
• Strong Python programming and system design skills.
• Strong cloud architecture and Kubernetes expertise.
• Strong understanding of LLM systems and production constraints.
• Strong debugging and production troubleshooting skills.
• Strong problem-solving and performance optimization skills.

Qualification And Education:

• Bachelor s or Master s degree in Computer Science or related field preferred.
• Strong production engineering experience in AI/ML systems required.