ML Ops Engineer

Overview

We are seeking a highly skilled MLOps Engineer with proven experience in Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) pipelines, and multi-cloud operations across AWS, Azure, and Google Cloud. This role focuses on operationalizing state-of-the-art LLM solutions — from fine-tuning and deployment to CI / CD automation, observability, and cost-optimized scaling — ensuring AI systems are production-ready, secure, and reliable. You will collaborate with Data Scientists, ML Engineers, and DevOps teams to deliver enterprise-grade Generative AI solutions with strong governance and scalability.

Key Responsibilities

Generative AI, LLM & Agentic Systems

• Implement and manage Generative AI pipelines, covering data preprocessing, LLM training, fine-tuning, and deployment.
• Design and integrate agentic workflows for reasoning, retrieval, and multi-agent collaboration using modern LLM frameworks.
• Build and optimize RAG pipelines leveraging vector databases (e.g., Qdrant) for retrieval-augmented inference.
• Develop tools for LLM evaluation, hallucination detection, and prompt optimization in production settings.
• Proven experience in LangChain and LangGraph for building complex LLM applications.
• Optimize inference performance, scaling strategies, and cost efficiency across cloud providers.
• Design and implement ETL processes and CI / CD pipelines for Generative AI workloads on AWS and Azure.
• Implement model monitoring, logging, versioning, and automated retraining for production readiness.
• Manage infrastructure using IaC tools (e.g., Terraform, CloudFormation) and container orchestration (e.g., Kubernetes, EKS, AKS, GKE).
• Ensure security, compliance, and observability in AI systems using tools like Prometheus, Grafana, and CloudWatch.
• Establish ML governance frameworks for model lineage, reproducibility, and auditability.
• Partner with Data Scientists to transition experimental LLM models into production-ready services.
• Establish best practices for RAG pipeline security, cost management, and cloud resource optimization.
• Contribute to internal knowledge sharing and drive LLMOps culture adoption within the organization.

Required Qualifications

Preferred Skills

Application

Ready to take the next big step in your career? If you're ready to bring your talent to a company that values innovation, collaboration, and excellence, we want to hear from you.

Send your resume to and let’s start the conversation.

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