Senior MLOps Engineer

Aligned Automation; Pune District; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

Aligned Automation Services Pvt Ltd is seeking a skilled MLOps Engineer to design and maintain scalable ML platforms. You will train, deploy, monitor, and manage ML models while optimizing CI/CD pipelines and toolchains in a fast-paced environment.

You will containerize workloads, orchestrate with Kubernetes, and build end-to-end ML pipelines using Kubeflow, MLflow, and Airflow. Strong scripting, API exposure, and security practices are essential for success.

Qualifications

• Proficient in Python with hands-on ML/AI workflow knowledge.
• Strong experience with Docker, Kubernetes, Helm, and containerized deployments.
• Solid understanding of Git, branching strategies, merge requests, and CI/CD practices.
• Experience with MLflow, Kubeflow, Airflow, or similar MLOps tools.
• Familiarity with Spark/PySpark and distributed data processing.
• Knowledge of REST APIs, FastAPI, Flask, and microservices architecture.
• Experience with Linux, Bash scripting, and automation.
• Awareness of model monitoring, drift detection, feature stores, and model governance.
• Experience working with SQL and NoSQL databases.
• Strong software engineering fundamentals, patterns, testing, and code quality.

Responsibilities

• Design, build, and maintain scalable MLOps platforms for training, deploying, monitoring, and managing ML models.
• Develop and optimize CI/CD pipelines for ML applications using GitLab CI, Jenkins etc.
• Containerize applications using Docker and orchestrate workloads using Kubernetes.
• Build and maintain ML pipelines using Kubeflow, MLflow, Airflow, Argo Workflows, or similar tools.
• Implement model versioning, experiment tracking, model registry, and reproducible ML workflows.
• Deploy ML models as REST/gRPC APIs using FastAPI, Flask, or similar frameworks.
• Configure monitoring, logging, and alerting using Prometheus, Grafana, Dynatrace, or Splunk.
• Optimize model serving performance, scalability, and resource utilization.
• Work closely with Data Scientists, Data Engineers, Platform Engineers, and Software Developers to productionize ML solutions.
• Ensure platform security, governance, and compliance following DevSecOps best practices.
• Troubleshoot production issues and perform root cause analysis for ML workloads.

Skills

Python

Docker

Kubernetes

Git

CI/CD

Kubeflow

MLflow

Airflow

Argo Workflows

REST APIs

FastAPI/Flask

Linux

Bash

Model monitoring

SQL/NoSQL

Spark/PySpark

DevSecOps

Tools

Docker

Kubernetes

Helm

Git

GitLab CI

Jenkins

Job description

Aligned Automation Services Pvt Ltd | Full time

At Aligned Automation, we livebyour"Better Together"philosophy to build a betterworld. As a strategic service provider to Fortune 500 companies, we helpdigitize enterprise operations and drive impactful business strategies. Our purposegoes beyond projects—we strive to deliver meaningful, sustainable change thatshapes a more optimistic and equitable future.

Our culture is deeply rooted inour4Cs—Care,Courage,Curiosity,andCollaboration—ensuring that each employee is empowered to grow,innovate, and thrive in an inclusive workplace.

About the Role

Key Responsibilities

• Design, build, and maintain scalable MLOps platforms for training, deploying, monitoring, and managing machine learning models.
• Develop and optimize CI/CD pipelines for ML applications using GitLab CI, Jenkins etc.
• Containerize applications using Docker and orchestrate workloads using Kubernetes.
• Build and maintain ML pipelines using Kubeflow, MLflow, Airflow, Argo Workflows, or similar orchestration tools.
• Implement model versioning, experiment tracking, model registry, and reproducible ML workflows.
• Deploy ML models as REST/gRPC APIs using FastAPI, Flask, or similar frameworks.
• Configure monitoring, logging, and alerting using Prometheus, Grafana, Dynatrace, or Splunk.
• Optimize model serving performance, scalability, and resource utilization.
• Work closely with Data Scientists, Data Engineers, Platform Engineers, and Software Developers to productionize ML solutions.
• Ensure platform security, governance, and compliance following DevSecOps best practices.
• Troubleshoot production issues and perform root cause analysis for ML workloads.

Required Technical Skills

• Strong programming experience in Python.
• Hands‑on experience with Docker, Kubernetes, Helm, and containerized deployments.
• Strong knowledge of Git, branching strategies, merge requests, and CI/CD.
• Experience with MLflow, Kubeflow, Airflow, or similar MLOps tools.
• Experience with Spark/PySpark and distributed data processing.
• Knowledge of REST APIs, FastAPI, Flask, and microservices architecture.
• Experience with Linux, Bash scripting, and automation.
• Familiarity with model monitoring, drift detection, feature stores, and model governance.
• Experience working with SQL and NoSQL databases.
• Strong understanding of software engineering principles, design patterns, testing, and code quality.

Preferred Skills

• Experience with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, and AI orchestration frameworks such as LangChain or langfuse.
• Experience with NVIDIA GPU workloads and model optimization.
• Knowledge of Kafka, RabbitMQ, or event‑driven architectures.
• Exposure to feature stores such as Feast.
• Experience with OpenShift, Red Hat ecosystem, or enterprise Kubernetes platforms.
• Knowledge of security scanning tools such as Trivy, SonarQube, and vulnerability management.