Machine Learning ( MLOps) & Data Engineer

ACL Digital; Pune District; On-site; INR 2,400,000 - 3,400,000; Full time

Job summary

ACL Digital in Pune, India, is seeking a Senior Machine Learning, MLOps and Data Engineer to design, deploy, and scale enterprise data pipelines and ML lifecycles across cloud environments.

You will work with Spark, Databricks, Airflow, Docker, Kubernetes, and CI/CD tools to build robust pipelines and production ML deployments. Ideal candidates have 5–7 years in ML engineering, data engineering, or related fields, with strong communication and multi-project prioritization.

Qualifications

• Bachelor’s or Master’s degree in CS/SE/DS/AI or related field.
• 5–7 years hands-on ML engineering, MLOps, data engineering, or software engineering.
• Proven experience with distributed systems like Spark, Kafka, Hadoop or Flink.
• Experience building large-scale data pipelines using Python, Databricks, Airflow.
• Hands-on experience with AWS, Azure, or GCP.
• Strong Docker, Kubernetes, and CI/CD (Azure DevOps, GitHub Actions, or Jenkins).
• Strong OO design, patterns, testing, and secure coding.
• ML lifecycle experience: feature management, experiment tracking, production deployment.
• Excellent problem-solving, communication, and multi-project prioritization.

Responsibilities

• Pipeline & Architecture Engineering: Design, build, and maintain large-scale distributed data pipelines.
• ML Ops & Model Lifecycle Management: Implement end-to-end MLOps, tracking, feature stores, automated experiments, production deployment.
• Infrastructure & CI/CD: Establish automated deployment pipelines via Azure DevOps, GitHub Actions, or Jenkins; containerize with Docker and Kubernetes.
• Cloud & Big Data Infrastructure: Manage distributed data workloads on AWS, Azure, or GCP with Spark, Kafka, or Flink.
• Software Engineering Standards: Enforce high code-quality with VCS, tests, secure coding, design patterns, peer reviews.
• Governance & Security: Ensure governance, privacy, security, and compliance.

Skills

Big Data Systems

Data Pipelines

Cloud Platforms

DevOps & Containers

Software Fundamentals

ML Lifecycle

Soft Skills

Education

Bachelor’s or Master’s degree in CS/SE/DS/AI or related field

Tools

Apache Spark

Kafka

Hadoop

Flink

Databricks

Airflow

Python

Docker

Kubernetes

AWS

Azure

GCP

Azure DevOps

GitHub Actions

Jenkins

Job description

Job Title: Machine Learning ( MLOps) & Data Engineer
Experience: 5 - 7 Years

Position Summary

We are seeking a highly skilled Senior Machine Learning & MLOps / Data Engineer with 57 years of enterprise experience to join our CIP project team. In this role, you will design, deploy, and scale enterprise data engineering pipelines, automate ML model lifecycles, and establish robust MLOps practices across modern cloud environments.

Key Responsibilities

• Pipeline & Architecture Engineering: Design, build, and maintain large-scale distributed data engineering pipelines using Python, Spark, Databricks, and orchestration engines like Apache Airflow.
  
• MLOps & Model Lifecycle Management: Implement end-to-end MLOps solutions, including model lifecycle tracking, feature store integration, automated experimentation, and robust production deployment strategies.
  
• Infrastructure & CI/CD: Establish automated deployment pipelines (CI/CD) via Azure DevOps, GitHub Actions, or Jenkins. Containerize applications using Docker and Kubernetes.
  
• Cloud & Big Data Infrastructure: Manage distributed data workloads using cloud platforms (AWS, Azure, or GCP) alongside technologies like Apache Spark, Kafka, or Flink.
  
• Software Engineering Standards: Enforce high code-quality standards through version control, automated testing, secure coding practices, design patterns, and peer code reviews.
  
• Governance & Security: Ensure all data and model infrastructure adheres to enterprise data governance, privacy, security, and compliance protocols.
  

Qualifications & Skill Requirements

Must-Have (Required)

• Education: Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Science, Artificial Intelligence, or a related field.
  
• Experience: 5–7 years of hands‑on experience in Machine Learning Engineering, MLOps, Data Engineering, or Software Engineering.
  
• Big Data Systems: Proven experience with distributed systems like Apache Spark, Kafka, Hadoop, or Flink.
  
• Data Pipelines: Proven expertise building and managing large-scale data pipelines using Python, Databricks, Airflow, or similar stacks.
  
• Cloud Platforms: Hands‑on experience with AWS, Azure, or GCP.
  
• DevOps & Containers: Strong proficiency in Docker, Kubernetes, and continuous integration/delivery (Azure DevOps, GitHub Actions, or Jenkins).
  
• Software Fundamentals: Deep understanding of object-oriented design, design patterns, testing strategies, and secure development.
  
• ML Lifecycle: Hands‑on experience in feature management, experiment tracking, and production model deployment.
  
• Soft Skills: Excellent problem‑solving, communication, and multi-project prioritization skills.
  

Nice-to-Have (Desired)

• Certifications: Professional certifications in Cloud (AWS/Azure/GCP), Data Engineering, MLOps, or Machine Learning.
  
• Advanced Data Platforms: Experience with Snowflake, Delta Lake, Feature Stores, or Vector Databases.
  
• AI & Machine Learning Frameworks: Exposure to TensorFlow, PyTorch, Scikit-learn, MLflow, or Generative AI / LLM / RAG / Agentic AI solutions.
  
• IaC & Observability: Familiarity with Terraform/Bicep/CloudFormation and monitoring tools like Grafana, Prometheus, ELK, or Datadog.
  
• Leadership & Process: Experience working in Agile/Scrum environments and mentoring junior engineers.