Machine Learning Engineer (AWS / MLOps / Data Engineering)

Machine Learning Engineer (AWS / MLOps / Data Engineering)

Location: Remote (USA) 
Employment Type: Long term contract (12+ Months) 

About the Opportunity

We are seeking a highly skilled Machine Learning Engineer to build, deploy, and scale production-grade machine learning solutions in a cloud-native environment. This role is ideal for an engineer who enjoys solving complex business challenges through robust software development, modern data architectures, and scalable ML platforms.

The successful candidate will work closely with Data Scientists, Data Engineers, and Architecture teams to transform machine learning models into real-world business applications. This position focuses on engineering excellence, production deployment, automation, and operationalizing machine learning products rather than research-oriented model development.

Key Responsibilities

• Design and implement scalable machine learning architectures on AWS.
• Build and maintain end-to-end ML pipelines supporting model training, deployment, monitoring, and lifecycle management.
• Develop real-time inference services and batch prediction workflows.
• Create and enhance feature engineering pipelines and feature store ecosystems.
• Collaborate with Data Science teams to operationalize machine learning models and maximize business impact.
• Develop cloud-native solutions leveraging modern AWS services and Infrastructure as Code (IaC) practices.
• Implement data processing frameworks for large-scale structured and unstructured datasets.
• Improve existing machine learning platforms for scalability, reliability, performance, and cost optimization.
• Partner with Data Engineering, Security, and Governance teams to ensure enterprise best practices.
• Participate in Agile development processes, including sprint planning, stand-ups, code reviews, and continuous improvement initiatives.

Required Qualifications

• Master's degree in Computer Science, Software Engineering, Data Science, or related technical field.
• 5+ years of software engineering, machine learning engineering, or cloud engineering experience.
• Strong experience building and deploying machine learning systems in production environments.
• Advanced knowledge of AWS cloud services and cloud-native application design.
• Expertise in:
  • Python
  • SQL
  • PySpark
  • Docker
  • CI/CD Pipelines
  • Infrastructure as Code (Terraform, CloudFormation, or similar)
• Experience working with both streaming and batch processing architectures.
• Strong understanding of software engineering principles, system design, and distributed systems.
• Experience within Agile software development environments.
• Exceptional analytical, troubleshooting, and problem-solving skills.
• Strong communication and collaboration abilities.

Preferred Skills

• Hands-on experience with AWS machine learning technologies such as SageMaker and related ML services.
• Experience managing feature stores and feature engineering frameworks.
• Background implementing MLOps practices and automated ML deployment pipelines.
• Experience supporting large-scale recommendation, prediction, optimization, or AI-driven platforms.
• Familiarity with model monitoring, observability, and ML governance.
• Exposure to enterprise-scale data platforms and analytics ecosystems.

Ideal Candidate Profile

We are looking for a technically strong engineer who:

• Possesses a solid software engineering foundation.
• Takes a systematic and structured approach to problem-solving.
• Learns new technologies quickly and adapts to evolving business needs.
• Understands how to translate machine learning concepts into reliable production systems.
• Enjoys collaborating across engineering, architecture, and data science teams.
• Thrives in a fast-paced Agile environment.
• Can clearly articulate technical decisions, architectural trade-offs, and implementation strategies.

Technical Environment

Cloud & ML Platforms

• AWS
• SageMaker
• Cloud-native ML Services

Programming & Data

• Python
• SQL
• PySpark

DevOps & MLOps

• Docker
• CI/CD
• Infrastructure as Code
• Machine Learning Operations (MLOps)

Data Processing

• Real-Time Streaming
• Batch Processing
• Feature Engineering
• Feature Stores