DevOps Engineer

Position Overview

We are seeking a specialized MLOps Engineer with mandatory experience in building and maintaining pipelines for evaluation and deployment of agentic systems . This role is critical to our growing AI/ML practice in Bengaluru, focusing on productionizing autonomous AI agents and multi-agent systems at enterprise scale.

Key Responsibilities

• Design and implement end-to-end MLOps pipelines specifically for agentic systems including autonomous agents, multi-agent frameworks, and LLM-based applications
• Build robust evaluation frameworks for agent performance, including metrics for task completion, decision quality, and agent collaboration
• Deploy and orchestrate agentic systems using containerization and microservices architectures
• Implement comprehensive monitoring for agent behavior, performance degradation, and system health
• Establish version control and experiment tracking for agent configurations, prompts, and model weights using MLflow (mandatory)
• Create automated testing pipelines for agent reasoning, tool usage, and edge case handling
• Build scalable infrastructure for agent deployment including API gateways, message queues, and state management
• Implement safety and guardrail mechanisms for production agent deployments
• Develop rollback and A/B testing strategies for agent updates and model changes
• Collaborate with ML researchers to productionize novel agent architectures

Required Technical Skills

MLOps & Agentic Systems (Mandatory):

• Proven experience building evaluation and deployment pipelines for agentic systems
• Expert-level proficiency with MLflow for experiment tracking, model registry, and deployment
• Experience with agent frameworks (LangChain, AutoGen, CrewAI, or similar)
• Knowledge of prompt engineering and LLM orchestration patterns
• Understanding of agent memory systems and state management

Programming & Infrastructure:

• Advanced Python programming with asyncio experience
• Docker and Kubernetes for containerized agent deployment
• Experience with message brokers (Redis, RabbitMQ, Apache Kafka)
• RESTful API design and implementation
• Microservices architecture patterns

Cloud & DevOps:

• AWS/Azure/GCP cloud platforms with focus on serverless and container services
• CI/CD pipelines (Jenkins, GitLab CI, GitHub Actions)
• Infrastructure as Code (Terraform, CloudFormation)
• Monitoring and observability tools (Prometheus, Grafana, OpenTelemetry)

ML & Data:

• Understanding of LLM fine-tuning and deployment
• Experience with vector databases (Pinecone, Weaviate, Chroma)
• Knowledge of RAG (Retrieval-Augmented Generation) systems
• Data pipeline tools (Apache Airflow, Prefect)

Qualifications

• Bachelor's/master's degree in computer science, Engineering, or related field
• 2-5 years of experience with mandatory focus on agentic systems MLOps
• Demonstrated experience with MLflow in production environments
• Strong software engineering fundamentals and design patterns
• Experience with distributed systems and scalability challenges
• Understanding of AI safety and alignment considerations
• Excellent problem-solving and debugging skills

What We Offer

• Work on cutting-edge agentic AI systems for Fortune 500 clients
• Opportunity to shape MLOps practices for next-generation AI systems
• Competitive compensation with performance bonuses
• Comprehensive health and wellness benefits
• Flexible hybrid work arrangements
• Dedicated learning budget for conferences and certifications

Note: Applications without demonstrated experience in agentic systems MLOps and MLflow will not be considered.