MLOps Engineer

Codvo.ai; Pune District; On-site; INR 2,000,000 - 3,500,000; Full time

Job summary

A global technology services company is seeking an MLOps Engineer to design, deploy, and maintain production-grade ML workflows across AWS and Azure. The ideal candidate will architect MLOps pipelines and automate model deployment while collaborating with DevOps teams. Required qualifications include a Bachelor's degree and over 4 years of experience in DevOps/MLOps roles, with expertise in cloud services. This position offers an exciting opportunity to work in a dynamic environment, contributing to scalable AI solutions.

Qualifications

• 4+ years of experience in DevOps/MLOps roles with AWS and Azure deployments.
• Hands-on experience building edge services and API applications for real-time inference.
• Strong problem-solving skills in multi-cloud environments.
• Hands-on experience with ML workflows, model deployment and monitoring.
• Hands-on programming skills in Python and Node.js are a plus.

Responsibilities

• Architect and implement MLOps pipelines for ML model processes.
• Build and manage Infrastructure as Code (IaC) for multi-cloud environments.
• Develop CI/CD pipelines to automate deployments of microservices.
• Develop CI/CD pipelines for Python/FastAPI and Node.js services.
• Create edge API services for low-latency inference.
• Implement observability with Prometheus, Grafana, CloudWatch, and Azure Monitor.
• Collaborate with data scientists and DevOps teams to productionize AI solutions.
• Write clean production-grade code in Python and Node.js.

Skills

Cloud (AWS, Azure)

Python

Docker

Kubernetes

GitHub Actions

Terraform

Node.js

CI/CD

Education

Bachelor's degree in Computer Science, Engineering, or related field

Tools

AWS SageMaker

Azure ML

MLflow

Kubeflow

Terraform

Docker

Kubernetes

Jenkins

GitHub Actions

AWS CodePipeline

Azure DevOps

Seldon

KServe

Job description

Company Overview:

AtCodvo, software and people transformations go hand-in-hand. We are a global empathy‑led technology services company. Product innovation and mature software engineering are part of our core DNA. Respect, Fairness, Growth, Agility, and Inclusiveness are the core values that we aspire to live by each day. We continue to expand our digital strategy, design, architecture, and product management capabilities to offer expertise, outside‑the‑box thinking, and measurable results.

Job Summary:

As an MLOps Engineer, you'll design, deploy, and maintain production‑grade ML workflows across AWS and Azure using container orchestration, IaC, and CI/CD pipelines. You'll bridge DevOps and ML teams to automate model training, deployment, monitoring, and edge API services—ensuring reliable, scalable AI solutions.

Key Responsibilities:

• Architect and implement MLOps pipelines for ML model training, versioning, deployment, and monitoring using tools like MLflow, Kubeflow, SageMaker Pipelines, or Azure ML.
• Build and manage Infrastructure as Code (IaC) with Terraform for multi‑cloud environments, including AWS EKS/ECS/Lambda and Azure AKS/Azure Functions.
• Design containerized applications with Docker and orchestrate them on Kubernetes (EKS/AKS) for high‑availability ML inference and edge services.
• Develop CI/CD pipelines using Azure DevOps (ADO), GitHub Actions, AWS CodePipeline, or Azure Pipelines to automate deployments of Python/FastAPI microservices and Node.js backends.
• Create and optimize edge API applications (e.g., FastAPI‑based services) for low‑latency inference on AWS Lambda@Edge, Azure Functions, or ECS Fargate.
• Implement observability with Prometheus, Grafana, CloudWatch, Azure Monitor, and alerting for ML model drift, performance, and infrastructure health.
• Collaborate with data scientists and DevOps teams to productionize AI solutions, troubleshoot issues, and scale for high workloads.
• Write clean, production‑ready code in Python, Node.js, and Bash for automation scripts, ETL processes, and API gateways.

Required Qualifications:

• Bachelor's degree in Computer Science, Engineering, or related field.
• 4+ years of experience in DevOps/MLOps roles, with proven deployments on AWS and Azure.
• Expertise in:
• Cloud: AWS (EKS, ECS, Lambda, SageMaker, ECR) and Azure (AKS, Azure ML, Functions)
• IaC & Orchestration: Terraform, Docker, Kubernetes (EKS/AKS)
• Pipelines: Azure DevOps (ADO), Jenkins, GitLab CI, or AWS/Azure‑native tools
• Programming: Python (FastAPI, Pandas, Scikit‑learn), Node.js
• ML Ops: Model deployment, versioning, monitoring (e.g., Seldon, KServe)
• Hands‑on experience building edge services and API applications for real‑time inference.
• Strong problem‑solving skills in multi‑cloud environments.

Preferred Skills:

• Certifications: AWS Certified Machine Learning – Specialty, Azure AI Engineer Associate, CKA/CKAD, Terraform Associate.
• Experience with vector databases (Pinecone, FAISS), serverless ML, or GenAI fine‑tuning.
• Knowledge of React.js for dashboarding ML metrics.