AI Platform Engineer

Atlas Copco; Pune District; On-site; INR 1,800,000 - 2,400,000; Full time

Benefits offered by this job

Career growth

Global opportunities

Work-life balance

Collaborative culture

Job summary

Atlas Copco in Pune, India seeks a data platform engineer to design and build a scalable data and AI platform supporting batch and streaming workloads. You will standardize infrastructures, frameworks, and best practices to accelerate data projects.

You will implement orchestration with Airflow, Prefect, or Dagster, develop APIs for self-service data access, and enable governance, security, and observability. The role requires 4+ years in data platform or related engineering and cloud experience.

Qualifications

• Bachelor's or Master's degree or equivalent practical experience.
• 4+ years of experience in data platform, backend, infrastructure, or related engineering roles.
• Strong experience designing and building scalable distributed systems.
• Deep understanding of data pipelines, storage systems, APIs, and data architecture.
• Hands-on experience with cloud platforms such as AWS, GCP, or Azure.
• Experience with orchestration tools such as Airflow, Prefect, or Dagster.
• Strong knowledge of data technologies such as Spark, Kafka, Snowflake, or BigQuery.
• Experience with Infrastructure as Code tools such as Terraform or similar.
• Understanding of platform reliability, observability, security, and data governance.
• Experience building reusable platforms, frameworks, or internal developer tools.

Responsibilities

• Design and build a scalable data and AI platform supporting batch and streaming workloads.
• Establish standardized infrastructure, frameworks, and best practices for data and AI workloads.
• Build and manage orchestration frameworks such as Airflow, Prefect, or Dagster, including reusable pipeline templates.
• Enable self-service data access through APIs, query engines, and semantic layers.
• Develop infrastructure to support AI/ML training, experimentation, deployment, reproducibility, and model versioning.
• Implement platform-wide logging, monitoring, alerting, governance, security, and access controls.
• Define and maintain SLAs for platform services and data pipelines.
• Standardize datasets as discoverable, documented, versioned, and reusable data products.
• Build internal platform capabilities and tooling that improve developer experience and engineering productivity.
• Design systems that scale with increasing data volumes and concurrent workloads without frequent re-architecture.
• Collaborate with engineering, data, and business teams to align platform capabilities with business and data use cases.

Job description

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• Design and build a scalable data and AI platform supporting batch and streaming workloads.
• Establish standardized infrastructure, frameworks, and best practices for data and AI workloads.
• Build and manage orchestration frameworks such as Airflow, Prefect, or Dagster, including reusable pipeline templates.
• Enable self-service data access through APIs, query engines, and semantic layers.
• Develop infrastructure to support AI/ML training, experimentation, deployment, reproducibility, and model versioning.
• Implement platform-wide logging, monitoring, alerting, governance, security, and access controls.
• Define and maintain SLAs for platform services and data pipelines.
• Standardize datasets as discoverable, documented, versioned, and reusable data products.
• Build internal platform capabilities and tooling that improve developer experience and engineering productivity.
• Design systems that scale with increasing data volumes and concurrent workloads without frequent re-architecture.
• Collaborate with engineering, data, and business teams to align platform capabilities with business and data use cases.

To Succeed, You Will Need:

• Bachelor's or master's degree, or equivalent practical experience.
• 4+ years of experience in data platform, backend, infrastructure, or related engineering roles.
• Strong experience in data platform or infrastructure engineering.
• Proven experience designing and building scalable distributed systems.
• Deep understanding of data pipelines, storage systems, APIs, and data architecture.
• Hands-on experience with cloud platforms such as AWS, GCP, or Azure.
• Experience with orchestration tools such as Airflow, Prefect, or Dagster.
• Strong knowledge of data technologies such as Spark, Kafka, Snowflake, or BigQuery.
• Experience with Infrastructure as Code tools such as Terraform or similar.
• Understanding of platform reliability, observability, security, and data governance.
• Experience building reusable platforms, frameworks, or internal developer tools.

Nice to Have

• Exposure to the product development lifecycle.
• Experience working in Agile environments such as Scrum or Kanban.
• Experience with data observability tools.
• Experience supporting ML/AI workflows in production.
• Exposure to embeddings, vector search, or Knowledge Graph technologies.

In return, we offer you:

• Plenty of opportunities to grow and develop.
• A culture known for respectful interaction, ethical behaviour and integrity.
• Potential to see your ideas realized and to make an impact.
• New challenges and new things to learn every day.
• Access to global job opportunities, as part of the Atlas Copco Group.
• Support for maintaining a healthy work-life balance, including vacation, personal, and sick time.

Uniting curious minds
Behind every innovative solution, there are people working together to transform the future. With careers sparked by initiative and lifelong learning, we unite curious minds, and you could be one of them.