We are seeking a hands-on Machine Learning Engineer to design, develop, deploy, and maintain scalable AI and machine learning solutions that drive measurable business impact. You will work independently and collaboratively to build production-grade ML pipelines, data solutions, and analytics platforms, leveraging modern frameworks, cloud infrastructure, and best practices. The role offers opportunities to work across cross-functional teams, translating real-world requirements into practical, secure, and high-performing AI systems.
Key Responsibilities
- Design, develop, test, and deploy high-quality, scalable, and maintainable machine learning and data science solutions.
- Execute the full ML lifecycle, from feature engineering and model development to deployment, monitoring, and continuous improvement.
- Build, transform, and map data across pipelines using modern tools and technologies.
- Develop robust specifications, metadata documentation, and operational feasibility plans for ML and data solutions.
- Implement MLOps and DevSecOps best practices, including CI/CD, automated testing, model versioning, observability, and reproducibility.
- Participate in code reviews, sign-off on features, and validate vulnerability fixes.
- Improve data solution quality, performance, reliability, and scalability, integrating external or internal data sources to create reusable assets.
- Collaborate with cross-functional teams to deliver data-informed strategies, ensuring compliance with governance, privacy, and security standards.
- Stay current with industry trends, cloud-native services, AI/ML technologies, and emerging best practices to influence architecture and solution design.
- Mentor and support other engineers in data engineering, ML development, and pipeline optimization.
Preferred Experience:
- Hands-on experience designing, developing, and deploying machine learning models in production.
- Strong programming skills in Python and R, and experience with ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
- Experience with data science platforms, cloud services (AWS S3, Lambda, SageMaker, Step Functions, Bedrock), Linux, and containerized environments.
- Knowledge of AI/GenAI development patterns, including Retrieval-Augmented Generation (RAG) and other modern architectures.
- Understanding of data modeling, data governance, and security best practices.
- Experience with testing frameworks, QA processes, and software lifecycle management in complex production environments.
- Ability to write data mapping, metadata specifications, and functional documentation for medium to large features.
- Strong problem-solving skills, technical leadership, and a focus on continuous learning and improvement.
