Machine Learning Engineer

Employment Type: Full-Time

Position Overview

ShyftLabs is seeking an experienced Machine Learning Engineer to join our growing team in Atlanta. You will be responsible for designing, building, and maintaining scalable ML infrastructure and deploying production-ready machine learning solutions that drive business impact. This role requires expertise in cloud platforms, ML operations, and end-to-end pipeline development.

ShyftLabs is a growing data product company founded in early 2020 and works primarily with Fortune 500 companies. We deliver digital solutions built to help accelerate the growth of businesses in various industries, by focusing on creating value through innovation.

Job Responsibilities

• Design, build, and maintain highly scalable, robust, and efficient cloud infrastructure using AWS services including SageMaker, EC2, S3, Lambda, and other ML-focused AWS offerings.
• Develop automation and orchestration of ML pipelines, integrating data ingestion, feature engineering, model training, and deployment processes.
• Build and deploy production-ready ML models for pricing optimization, operational efficiency, and predictive analytics applications.
• Implement natural language processing solutions and conversational AI systems.
• Collaborate with cross-functional teams including data scientists, software engineers, and business stakeholders to deliver end-to-end ML solutions.
• Optimize data processing pipelines and AWS resources to ensure low-latency, cost-effective operation.
• Implement monitoring, alerting, and failover strategies to ensure platform reliability and model performance.
• Stay updated with industry trends and best practices in MLOps, AWS cloud engineering, and machine learning infrastructure.

Key Attributes

• Customer-centric mindset: Passionate about delivering exceptional ML solutions that drive measurable business outcomes.
• Collaboration: Strong communication skills to work closely with cross-functional teams, translating business requirements into technical solutions.
• Problem-solving: Ability to identify and solve complex technical issues related to ML pipelines, cloud infrastructure, and scalability.
• Automation-first approach: Commitment to streamlining and automating ML processes for scalability and reliability.
• Adaptability: Ability to quickly adjust to new technologies and evolving business needs.
• Ownership and initiative: Comfortable taking ownership of key ML platform components and driving innovation.

Basic Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, Machine Learning, or a related quantitative field.
• 3+ years of experience in machine learning engineering or software engineering with a focus on ML infrastructure.
• Hands-on experience with AWS services including SageMaker, EC2, S3, Lambda, Glue, and other ML-focused AWS offerings.
• Proficiency in Python, SQL, and experience with ML frameworks (TensorFlow, PyTorch, scikit-learn).
• Experience with orchestration tools such as Apache Airflow, Kubeflow, or MLflow.
• Knowledge of CI/CD pipelines and DevOps tools for continuous integration and deployment.
• Familiarity with containerization and orchestration (Docker, Kubernetes).
• Experience with data processing frameworks (Spark, Pandas, Dask).
• Strong understanding of ML algorithms, model evaluation, and production deployment challenges.

Preferred Qualifications

• Experience with pricing optimization, recommendation systems, or operational analytics.
• Knowledge of natural language processing and conversational AI development.
• Experience with real-time ML inference and streaming data processing.
• Familiarity with A/B testing frameworks and experimentation platforms.

Company Information & Benefits

We are proud to offer a competitive salary alongside a strong healthcare insurance and benefits package. The role is preferably hybrid, with 3 days per week spent in the office. We pride ourselves on the growth of our employees, offering extensive learning and development resources.

ShyftLabs is an equal-opportunity employer committed to creating a safe, diverse, and inclusive workplace.