Machine Learning Solutions Architect

Job OverviewWe are seeking an experienced Machine Learning Engineer to join our team.In this role, you will design, develop, and deploy scalable production-ready machine learning systems and end-to-end pipelines on AWS.You will partner with data scientists, software engineers, and product teams to define requirements, select algorithms, and deliver impactful ML solutions.You will also architect, optimize, and maintain ML infrastructure, including data ingestion, model training, deployment, serving, monitoring, and lifecycle management using AWS services.As a key member of our team, you will lead the data preparation and feature engineering process, ensuring data quality, integrity, and scalability across large datasets.You will implement and optimize ML models, including supervised, unsupervised, deep learning, NLP, and recommendation systems, with a focus on performance, accuracy, and reliability.You will build and manage robust data pipelines and orchestration workflows to support ML systems at scale.You will integrate models into backend services and APIs, ensuring seamless interaction with applications and end users.Additionally, you will contribute to MLOps practices, including CI/CD for ML model registries, experiment tracking, automated retraining, and infrastructure-as-code provisioning.Key ResponsibilitiesDesign, develop, and deploy scalable production-ready machine learning systems and end-to-end pipelines on AWS.Partner with data scientists, software engineers, and product teams to define requirements, select algorithms, and deliver impactful ML solutions.Architect, optimize, and maintain ML infrastructure, including data ingestion, model training, deployment, serving, monitoring, and lifecycle management using AWS services.Lead the data preparation and feature engineering process, ensuring data quality, integrity, and scalability across large datasets.Implement and optimize ML models, including supervised, unsupervised, deep learning, NLP, and recommendation systems, with a focus on performance, accuracy, and reliability.Build and manage robust data pipelines and orchestration workflows to support ML systems at scale.Integrate models into backend services and APIs, ensuring seamless interaction with applications and end users.Contribute to MLOps practices, including CI/CD for ML model registries, experiment tracking, automated retraining, and infrastructure-as-code provisioning.Stay ahead of emerging trends in AI/ML, evaluating new research frameworks and tools to enhance product capabilities.Provide technical leadership and mentorship to junior engineers, guiding best practices throughout the ML lifecycle.Minimum QualificationsAdvanced written and oral English proficiency.Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, or a related field.7+ years of professional experience designing, building, and deploying machine learning models in production environments.Strong 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-learn.Proficiency with TypeScript for building ML-integrated backend services and automation workflows.Experience with Infrastructure-as-Code tools, Terraform, AWS CDK, or CloudFormation for deploying ML infrastructure.Strong knowledge of data processing and analysis tools, Pandas, NumPy, SQL, and orchestration workflows.Proven track record deploying ML systems into production and integrating them into real products or services at scale.Experience with containerization, Docker, orchestration, Kubernetes, and MLOps best practices.Preferred QualificationsExperience with large language models, LLMs, retrieval-augmented generation, RAG pipelines, or agentic AI systems.Expertise in deep learning, NLP, time-series forecasting, or computer vision.Familiarity with platforms such as MLflow, Kubeflow, or Amazon SageMaker for model lifecycle management.Contributions to open-source AI/ML projects or publications in the field.Understanding of data engineering workflows, ETL pipelines, and real-time data processing.