Machine Learning Engineer Position

About this role:Flatiron is a global software development company with a team of experts from diverse backgrounds and experiences.We unite our experts in a collaborative culture to deliver exceptional products and services for clients.In this forward-thinking environment, we provide industry-leading solutions to complex problems.Our team members enjoy a stimulating and rewarding environment where innovation, continuous learning, and professional growth are valued.This role involves working as part of a Scrum team, closely collaborating with software engineers, stakeholders, and contributors on projects.A key aspect of this position is attending meetings, actively participating in decision-making processes, and working together with other stakeholders.The role requires designing, developing, and deploying scalable, production-ready machine learning systems and end-to-end pipelines on AWS.This involves partnering with data scientists, software engineers, and product teams to define requirements, select algorithms, and deliver impactful ML solutions.The successful candidate will also be responsible for architecting, optimizing, and maintaining ML infrastructure – including data ingestion, model training, deployment, serving, monitoring, and lifecycle management – using AWS services.An important aspect of this role is leading the data preparation and feature engineering process, ensuring data quality, integrity, and scalability across large datasets.The ideal candidate will implement and optimize ML models (supervised, unsupervised, deep learning, NLP, recommendation systems) with a focus on performance, accuracy, and reliability.Additionally, they will build and manage robust data pipelines and orchestration workflows to support ML systems at scale.This role also involves integrating models into backend services and APIs, ensuring seamless interaction with applications and end users.The successful candidate will contribute to MLOps practices, including CI/CD for ML, model registries, experiment tracking, automated retraining, and infrastructure-as-code provisioning.They will utilize Infrastructure-as-Code tools such as Terraform, AWS CDK, or CloudFormation to build and maintain scalable, secure ML infrastructure.A key requirement for this role is staying ahead of emerging trends in AI/ML, evaluating new research, frameworks, and tools to enhance product capabilities.The ideal candidate will provide technical leadership and mentorship to junior engineers, guiding best practices throughout the ML lifecycle.Key responsibilities include:Designing, developing, and deploying scalable, production-ready machine learning systems and end-to-end pipelines on AWSPartnering with data scientists, software engineers, and product teams to define requirements, select algorithms, and deliver impactful ML solutionsArchitecting, optimizing, and maintaining ML infrastructure – including data ingestion, model training, deployment, serving, monitoring, and lifecycle management – using AWS servicesLeading the data preparation and feature engineering process, ensuring data quality, integrity, and scalability across large datasetsImplementing and optimizing ML models (supervised, unsupervised, deep learning, NLP, recommendation systems) with a focus on performance, accuracy, and reliabilityBuilding and managing robust data pipelines and orchestration workflows to support ML systems at scaleIntegrating models into backend services and APIs, ensuring seamless interaction with applications and end usersContributing to MLOps practices, including CI/CD for ML, model registries, experiment tracking, automated retraining, and infrastructure-as-code provisioningUtilizing Infrastructure-as-Code tools such as Terraform, AWS CDK, or CloudFormation to build and maintain scalable, secure ML infrastructureStaying ahead of emerging trends in AI/ML, evaluating new research, frameworks, and tools to enhance product capabilitiesProviding technical leadership and mentorship to junior engineers, guiding best practices throughout the ML lifecycleRequired skills and qualifications:Advanced written and oral English proficiencyBachelor's or Master's degree in Computer Science, Machine Learning, Data Science, or a related field7+ years of professional experience designing, building, and deploying machine learning models in production environmentsStrong hands-on experience with AWS for ML workflows (data pipelines, model training, deployment, and monitoring)Expert proficiency in Python and ML frameworks such as TensorFlow, PyTorch, and scikit-learnProficiency with TypeScript for building ML-integrated backend services and automation workflowsExperience with Infrastructure-as-Code tools (Terraform, AWS CDK, or CloudFormation) for deploying ML infrastructureStrong knowledge of data processing and analysis tools (Pandas, NumPy, SQL) and orchestration workflowsProven track record deploying ML systems into production and integrating them into real products or services at scaleExperience with containerization (Docker), orchestration (Kubernetes), and MLOps best practicesBenefits:Wellbeing SupportAt Flatiron, we value diversity, equity, and inclusion.We welcome candidates from all backgrounds and perspectives.We have a zero-tolerance policy towards discrimination.