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 offeringsDevelop automation and orchestration of ML pipelines, integrating data ingestion, feature engineering, model training, and deployment processesBuild and deploy production-ready ML models for pricing optimization, operational efficiency, and predictive analytics applicationsImplement natural language processing solutions and conversational AI systemsCollaborate with cross-functional teams including data scientists, software engineers, and business stakeholders to deliver end-to-end ML solutionsOptimize data processing pipelines and AWS resources to ensure low-latency, cost-effective operationImplement monitoring, alerting, and failover strategies to ensure platform reliability and model performanceStay updated with industry trends and best practices in MLOps, AWS cloud engineering, and machine learning infrastructure
Customer-centric mindset: Passionate about delivering exceptional ML solutions that drive measurable business outcomesCollaboration: Strong communication skills to work closely with cross-functional teams, translating business requirements into technical solutionsProblem-solving: Ability to identify and solve complex technical issues related to ML pipelines, cloud infrastructure, and scalabilityAutomation-first approach: Commitment to streamlining and automating ML processes for scalability and reliabilityAdaptability: Ability to quickly adjust to new technologies and evolving business needsOwnership 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 field3+ years of experience in machine learning engineering or software engineering with a focus on ML infrastructureHands-on experience with AWS services including SageMaker, EC2, S3, Lambda, Glue, and other ML-focused AWS offeringsProficiency in Python, SQL, and experience with ML frameworks (TensorFlow, PyTorch, scikit-learn)Experience with orchestration tools such as Apache Airflow, Kubeflow, or MLflowKnowledge of CI/CD pipelines and DevOps tools for continuous integration and deploymentFamiliarity 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 analyticsKnowledge of natural language processing and conversational AI developmentExperience with real-time ML inference and streaming data processingFamiliarity with A/B testing frameworks and experimentation platformsWe 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 environment. We encourage qualified applicants of all backgrounds including ethnicity, religion, disability status, gender identity, sexual orientation, family status, age, nationality, and education levels to apply. If you are contacted for an interview and require accommodation during the interviewing process, please let us know.
