Lead ML Ops

Evergent; Hyderabad; On-site; INR 1,500,000 - 2,000,000; Full time

Job summary

Evergent in Hyderabad is seeking a highly motivated Lead MLOps Engineer to lead the development and operation of MLOps infrastructure. You will build automated pipelines for deploying and managing AI/ML models, focusing on Conversational AI and NLP.

This position requires a strong technical background in MLOps, DevOps, and cloud technologies like AWS, Azure, or GCP. Candidates should have 7+ years of experience in software development and a Bachelor’s degree in Computer Science.

Qualifications

• 7+ years of overall experience in software development with a focus on MLOps principles.
• Extensive experience with containerization technologies (Docker) and orchestration platforms (Kubernetes).
• Strong skills with DevOps practices and CI/CD pipelines.

Responsibilities

• Architect, build, and maintain end-to-end CI/CD pipelines for deploying AI/ML models.
• Design and manage infrastructure for MLOps workflows, including Kubernetes and cloud resources.
• Implement robust model deployment strategies and monitoring solutions.

Skills

MLOps principles

DevOps practices

Cloud technologies

Pipeline design

Containerization (Docker)

Orchestration (Kubernetes)

CI/CD pipelines

Education

Bachelor’s degree in Computer Science or equivalent

Tools

AWS

Azure

GCP

Jenkins

GitLab CI

Terraform

Job description

We are seeking a highly motivated and experienced Lead MLOps Engineer to join our team and lead the development and operation of our Machine Learning Operations (MLOps) infrastructure. You will be responsible for building robust, scalable, and automated pipelines for deploying, monitoring, and managing our AI/ML models, with a particular focus on Conversational AI, RAG, NLP & LLM systems. This role requires a strong blend of technical expertise in MLOps principles, DevOps practices, and cloud technologies.

Responsibilities

• Pipeline Design & Development: Architect, build, and maintain end-to-end CI/CD pipelines for the deployment and operation of AI/ML models, specifically focusing on Conversational AI, RAG (Retrieval Augmented Generation), NLP (Natural Language Processing), and LLM (Large Language Model) systems.
• Infrastructure Management: Design and manage the underlying infrastructure required to support our MLOps workflows, including Kubernetes clusters, containerized environments, and cloud resources.
• Model Deployment & Monitoring: Implement robust model deployment strategies and monitoring solutions to ensure high availability, performance, and accuracy of deployed models.
• DevOps Practices: Champion DevOps best practices throughout the ML lifecycle, fostering a culture of automation, collaboration, and continuous improvement.
• Cloud Platform Expertise: Leverage cloud platforms (AWS, Azure, GCP) to build scalable and cost-effective MLOps solutions.
• Collaboration & Mentorship: Collaborate closely with data scientists, machine learning engineers, and software developers to ensure seamless integration of ML models into production environments. Mentor junior engineers in MLOps best practices.
• Performance Optimization: Identify and implement optimizations to improve the efficiency and performance of our MLOps pipelines and deployed models.
• Security & Compliance: Ensure that all MLOps processes and infrastructure adhere to security and compliance requirements.

Qualifications

• Education: Bachelor’s degree in Computer Science or equivalent degree with a strong foundation in AI/MLOperations and Data Science operations.
• Experience: 7+ years of overall experience in software development, with a focus on MLOps principles and practices.
• Containerization & Orchestration: Extensive experience with containerization technologies (Docker) and orchestration platforms (Kubernetes).
• DevOps Expertise: Strong skills with DevOps practices and CI/CD pipelines (e.g., Jenkins, GitLab CI, Azure DevOps).
• Cloud Proficiency: Proven experience working with cloud platforms and tools including Linux, Git, Docker, Terraform, Kubernetes, AWS, Azure, or GCP.
• ML Model Deployment & Monitoring: Experience with model deployment and monitoring tools and techniques.