ML engineer

Lead the end-to-end design, development, and deployment of scalable machine learning models and systems in production.

• Architect robust, high-performance ML infrastructure and data pipelines that support training, validation, and real-time inference.
• Drive technical decision-making, setting standards for code quality, model governance, and MLOps practices.
• Mentor and provide technical guidance to junior and mid-level engineers through code reviews, pairing, and knowledge sharing.
• Partner with data scientists to operationalize advanced research into reliable, production-ready services.
• Define and implement monitoring, observability, and automated retraining strategies to ensure model reliability and detect drift.
• Collaborate with product, engineering, and business leadership to shape ML roadmaps and translate strategic goals into technical deliverables.
• Evaluate and introduce emerging ML technologies, frameworks, and methodologies to keep the organization at the forefront of innovation.
• Own the technical health of ML systems, including performance optimization, cost efficiency, and scalability.

Bachelor’’s or Master’’s degree in Computer Science, Data Science, Engineering, or a related field.

• 10+ years of hands-on experience deploying machine learning models in production, with a track record of delivering large-scale systems.
• Expert-level programming skills in Python, with basic understanding in additional languages such as Java, Scala.
• Advanced understanding of data structures, algorithms, distributed systems, and software engineering principles.
• Extensive experience with cloud platforms (AWS) and containerization/orchestration (Docker, Kubernetes).

Deep experience with MLOps tooling (MLflow, Kubeflow, SageMaker, Vertex AI) and CI/CD for ML.

• Proven experience designing and maintaining large-scale data processing systems.
• Demonstrated experience leading technical projects and mentoring engineers.

Preferred Qualifications

– Advanced knowledge of deep learning, NLP, computer vision, or large language models.

• Experience with distributed training, model optimization, and high-throughput serving infrastructure.
• Understanding of ML frameworks such as TensorFlow, PyTorch, or scikit-learn.

Core Competencies

• Strategic thinking with strong analytical and problem-solving capabilities.
• Exceptional communication skills, with the ability to influence both technical and non-technical stakeholders.
• Proven technical leadership and mentorship abilities.
• Ability to navigate ambiguity, drive initiatives independently, and manage competing priorities.
• Strong ownership mindset and commitment to engineering excellence.