We are seeking an AWS ML Cloud Engineer to design, deploy, and optimize cloud-native machine-learning systems that power our next-generation predictive-automation platform. You will blend deep ML expertise with hands-on AWS engineering, turningdata into low-latency, high-impact insights. The ideal candidate commands statistics, coding, and DevOps—and thrives on shipping secure, cost-efficient solutions at scale.

Objectives of this role

• Design and productionize cloud ML pipelines (SageMaker, Step Functions, EKS) that advance predictive-automation roadmap
• Integrate foundation models via Bedrock and Anthropic LLM APIs to unlock generative-AI capabilities
• Optimize and extend existing ML libraries / frameworks for multi-region, multi-tenant workloads
• Partner cross-functionally with data scientists, data engineers, architects, and security teams to deliver end-to-end value
• Detect and mitigate data-distribution drift to preserve model accuracy in real-world traffic
• Stay current on AWS, MLOps, and generative-AI innovations; drive continuous improvement

Responsibilities

• Transform data-science prototypes into secure, highly available AWS services; choose and tune the appropriate algorithms, container images, and instance types
• Run automated ML tests/experiments; document metrics, cost, and latency outcomes
• Train, retrain, and monitor models with SageMaker Pipelines, Model Registry, and CloudWatch alarms
• Build and maintain optimized data pipelines (Glue, Kinesis, Athena, Iceberg) feeding online/offline inference
• Collaborate with product managers to refine ML objectives and success criteria; present results to executive stakeholders
• Extend or contribute to internal ML libraries, SDKs, and infrastructure-as-code modules (CDK / Terraform)

Skills and qualifications

• Primary technical skills
• AWS SDK, SageMaker, Lambda, Step Functions
• Machine-learning theory and practice (supervised / deep learning)
• DevOps & CI/CD (Docker, GitHub Actions, Terraform/CDK)
• Cloud security (IAM, KMS, VPC, GuardDuty)
• Networking fundamentals
• Java, Springboot, JavaScript/TypeScript & API design (REST, GraphQL)
• Linux administration and scripting
• Bedrock & Anthropic LLM integration
• Secondary / tool skills
• Advanced debugging and profiling
• Hybrid-cloud management strategies
• Large-scale data migration
• Impeccable analytical and problem-solving ability; strong grasp of probability, statistics, and algorithms
• Familiarity with modern ML frameworks (PyTorch, TensorFlow, Keras)
• Solid understanding of data structures, modeling, and software architecture
• Excellent time-management, organizational, and documentation skills
• Growth mindset and passion for continuous learning

Preferred qualifications

• 10+ years of Software Experience
• 3+ years in an ML-engineering or cloud-ML role (AWS focus)
• Proficient in Python (core), with working knowledge of Java or R
• Outstanding communication and collaboration skills; able to explain complex topics to non-technical peers
• Proven record of shipping production ML systems or contributing to OSS ML projects
• Bachelor’s (or higher) in Computer Science, Data Engineering, Mathematics, or a related field
• AWS Certified Machine Learning – Specialty and/or AWS Solutions Architect – Associate a strong plus