Senior Data Engineer

ACS International India Pvt. Ltd. (ACSII); Maharashtra; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

ACS International India Pvt. Ltd. (ACSII) seeks a Senior Data Engineer to design, build, and maintain scalable data pipelines in AWS. You will shape MLOps practices across cloud and on-prem environments, drive model lifecycle governance, and lead tool integration for CI/CD and data workflows.

You will mentor junior engineers, collaborate with data scientists and DevOps teams, and translate business needs into scalable AI/ML solutions while evaluating emerging technologies.

Qualifications

• Strong hands-on data engineering across pipelines, cloud infra and production deployments.
• AWS required; Azure and/or GCP are a strong plus.
• Proficiency in Python and modern data/ML tooling.
• CI/CD, containerization and DevOps applied to data/AI systems.
• 7+ years of progressive data engineering experience with ownership.
• Track record leading technical projects or workstreams.

Responsibilities

• Design, develop, and maintain scalable data pipelines using AWS services.
• Define best practices for model lifecycle management, including versioning and deployment.
• Lead selection and integration of tools for CI/CD, model registry, and data pipelines.
• Automate workflows for data ingestion, preprocessing, training, validation, and deployment.
• Establish monitoring and alerting for model performance, data drift, and infrastructure health.
• Mentor junior engineers and collaborate with data scientists, software developers, and DevOps.

Skills

Data pipelines

Cloud infrastructure

Data architecture

Python

CI/CD practices

DevOps for AI

LLM observability

Distributed systems

AWS

Technical leadership

Job description

Position: Senior Data Engineer

Location: Pune

Job Responsibilities

• Design, develop, and maintain scalable data pipelines using AWS services.
• Design and implement scalable, secure, and resilient MLOps architecture across cloud and on-prem environments
• Define best practices for model lifecycle management, including versioning, deployment, monitoring, and governance
• Lead the selection and integration of tools for CI/CD, model registry, feature store, and data pipelines
• Automate workflows for data ingestion, preprocessing, training, validation, and deployment
• Establish monitoring and alerting systems for model performance, data drift, and infrastructure health
• Ensure reproducibility, traceability, and compliance with regulatory standards
• Work closely with data scientists, data engineers, software developers, and DevOps teams
• Mentor junior engineers and promote a culture of continuous learning and innovation
• Translate business requirements into scalable AI/ML solutions
• Evaluate emerging technologies and frameworks to enhance MLOps capabilities

• Design, develop, and maintain scalable data pipelines using AWS services.
• Design and implement scalable, secure, and resilient MLOps architecture across cloud and on-prem environments
• Define best practices for model lifecycle management, including versioning, deployment, monitoring, and governance
• Lead the selection and integration of tools for CI/CD, model registry, feature store, and data pipelines
• Automate workflows for data ingestion, preprocessing, training, validation, and deployment
• Establish monitoring and alerting systems for model performance, data drift, and infrastructure health
• Ensure reproducibility, traceability, and compliance with regulatory standards
• Work closely with data scientists, data engineers, software developers, and DevOps teams
• Mentor junior engineers and promote a culture of continuous learning and innovation
• Translate business requirements into scalable AI/ML solutions
• Evaluate emerging technologies and frameworks to enhance MLOps capabilities

Ideal Candidate Will Have

• Strong, hands-on experience in data engineering — pipelines, cloud infrastructure, data architecture, and production-grade deployments
• Demonstrated experience with cloud platforms (AWS required; Azure and/or GCP a strong plus)
• Comfort working in AI and agentic AI contexts — experience with LLM infrastructure, agent frameworks, evaluation pipelines, or observability tooling is highly valued
• Proficiency in Python and modern data/ML engineering tooling
• Experience with CI/CD practices, containerization, and DevOps principles applied to data and AI systems
• Ability to operate across the full stack — from raw data through model serving infrastructure
• 7+ years of progressive data engineering experience, with demonstrated growth in scope and technical ownership.
• Track record of leading technical projects or workstreams, not just executing within them.
• Experience in distributed or global team environments is a plus