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.