Senior AI Platform Engineer

A.P. Moller Maersk; Bengaluru; On-site; INR 900,000 - 1,800,000; Full time

Job summary

A.P. Moller Maersk in Bengaluru seeks an experienced AI/ML Platform Engineer to design and implement scalable ML platforms, data systems, and automation that enable AI and ML teams to build, deploy, monitor, and operate solutions securely.

You will work across cloud infrastructure, DevOps, MLOps, Kubernetes, CI/CD, and observability to deliver reliable, self-service capabilities and governance for enterprise AI workloads.

Qualifications

• Experience designing scalable ML platforms and data systems.
• Strong understanding of ML pipelines, deployment, and governance.
• Proficiency in cloud-native infra, automation and security controls.

Responsibilities

• Design and implement scalable ML solutions and data systems end-to-end.
• Translate business needs into robust data engineering solutions.
• Develop and refine ML pipelines, model deployment, and monitoring.
• Apply innovative problem-solving to improve data platforms and outcomes.
• Resolve challenges in data models and deployment to meet performance goals.
• Mentor teammates via code reviews and knowledge sharing.
• Communicate decisions clearly to technical and non-technical stakeholders.
• Ensure production readiness with testing, observability and scalability.
• Drive cross-team initiatives to improve workflows and collaboration.

Skills

AI/ML Platform

Cloud Platform Engineering

Programming

MLOps

DevSecOps

Distributed Systems

Tools

Kubernetes

AWS

Terraform

Docker

Job description

Job Summary

AI/ML Platform Engineering focuses on building scalable engineering platforms, infrastructure, automation, and operational capabilities that enable AI and Machine Learning teams to develop, deploy, integrate, monitor, and operate AI/ML solutions efficiently and securely. AI/ML platforms provide standardized tools and services for model lifecycle management, MLOps, model serving, Generative AI, LLM applications, agentic AI, experimentation, deployment, observability, and governance. AI/ML Platform Engineering combines cloud infrastructure, software engineering, DevOps, MLOps, Kubernetes, automation, and AI technologies to create reliable and self-service platforms that accelerate enterprise AI adoption.

Data AI/ML (Artificial Intelligence and Machine Learning) Engineering involves the use of algorithms and statistical models to enable systems to analyze data, learn patterns, and make data-driven predictions or decisions without explicit human programming. AI/ML applications leverage vast amounts of data to identify insights, automate processes, and solve complex problems across a wide range of fields, including healthcare, finance, e-commerce, and more. AI/ML processes transform raw data into actionable intelligence, enabling automation, predictive analytics, and intelligent solutions. Data AI/ML combines advanced statistical modeling, computational power, and data engineering to build intelligent systems that can learn, adapt, and automate decisions.

Responsibilities

• Independently design and implement scalable machine learning solutions and data systems, ensuring end to end workflows, large scale analytics and reliability
• Collaborate with stakeholders to translate business needs into data engineering solutions, evaluate user journeys and challenge business requirements to ensure seamless, value driven delivery and integration of solutions
• Implement and refine feature engineering, monitoring, ML pipelines, deploy models in production, and address challenges in data pipelines
• Apply innovative problem-solving techniques, leveraging advanced methodologies to find unique approaches to complex problems and improve outcomes
• Investigate and resolve complex challenges in data models and deployment to ensure reliable solutions that meet performance benchmarks
• Mentor team members through code reviews, pairing sessions, knowledge-sharing sessions, and contribute to Communities of Practice
• Communicate technology, infrastructure, and deployment decisions clearly to both technical and non-technical stakeholders while maintaining detailed documentation to ensure reproducibility, scalability, and understanding
• Ensure readiness for production releases, focusing on testing, monitoring, observability, and maintaining scalability and reusability of models for future projects
• Drive cross-team and cross-discipline initiatives to optimize workflows, remove redundant applications and processes, share best practices, and enhance collaboration between teams
• Demonstrate awareness of shared platform capabilities and actively identify opportunities to leverage them in designing efficient and scalable data engineering solutions

Core Skills

• AI Machine Learning: Creating AI-powered solutions using Generative AI, Agentic AI and machine learning technologies to solve business problems and automate processes. Proficiency Level: Proficient
• Cloud Platform Engineering: Designing, deploying and operating secure, scalable and highly available cloud-native platforms on AWS. Proficiency Level: Advanced
• Programming: Writing production-grade applications, platform services and automation using languages such as Python, Java and SQL. Proficiency Level: Advanced
• MLOps AI Operations: Automating the end-to-end lifecycle of AI models including deployment, monitoring, governance and optimization. Proficiency Level: Advanced
• DevSecOps Automation: Using CI/CD pipelines, Infrastructure as Code and security controls to automate software development and platform operations. Proficiency Level: Advanced
• Distributed Systems: Designing scalable, resilient and fault-tolerant systems capable of supporting enterprise AI workloads. Proficiency Level: Advanced

Specialized Skills

• Generative AI LLMs: Building applications using Large Language Models, Retrieval Augmented Generation (RAG), prompt engineering and AI agents.
• Agentic AI Frameworks: Developing autonomous and multi-agent solutions using modern orchestration frameworks and enterprise AI patterns.
• AI Platform Architecture: Designing reusable AI platform capabilities including model serving, inference orchestration and governance frameworks.
• AWS Cloud Services: Leveraging services such as EKS, Lambda, Bedrock, API Gateway, S3, IAM, Step Functions and CloudWatch.
• Containerization Kubernetes: Deploying, managing and scaling containerized applications using Docker and Kubernetes.
• Infrastructure as Code: Automating infrastructure provisioning and platform operations using Terraform and cloud-native tooling.
• Observability Reliability Engineering: Implementing monitoring, logging, tracing and performance optimization for platform services.
• Security Governance: Applying enterprise security practices including IAM, secrets management, compliance and responsible AI controls.
• API Integration Engineering: Building scalable APIs and integrating enterprise platforms, cloud services and AI solutions.
• Technical Documentation: Creating architecture documents, operational runbooks and technical standards for enterprise platforms.