We are seeking an Expert Machine Learning Engineer to lead the design and implementation of complex ML systems that power strategic business capabilities. In this role, you will be responsible for defining technical direction, solving high-impact problems, and influencing both architectural and product decisions across teams. You’ll serve as a subject matter expert in applied machine learning and scalable systems, collaborating with engineers, scientists, and leadership to drive innovation and operational excellence.
This is an ideal role for experienced engineers who bring both deep technical expertise and strategic vision to their ML work.
Lead design, development, and deployment of scalable ML models and systems in production.
Architect robust data pipelines and machine learning workflows for large-scale applications.
Partner with product, engineering, and executive stakeholders to align ML efforts with long-term business goals.
Perform model performance analysis, monitoring, drift detection, and implement continuous retraining strategies.
Set coding and modeling standards; drive engineering excellence across ML development.
Serve as a mentor and technical advisor to junior and mid-level engineers and data scientists.
Conduct research and evaluate emerging tools, technologies, and methodologies to keep systems and teams cutting-edge.
Education:
Bachelor’s degree in Computer Science, Machine Learning, Engineering, or a related technical field.
Master’s or PhD preferred in AI, Machine Learning, or a similar advanced field.
Experience:
7–10 years of industry experience in machine learning, with a proven track record of building production-grade ML solutions at scale.
Expert-level proficiency in Python and advanced ML libraries (e.g., TensorFlow, PyTorch, JAX, Scikit-learn).
Deep understanding of classical ML, deep learning, NLP, or other specialized domains.
Hands-on experience with distributed computing frameworks (e.g., Spark, Ray) and real-time ML systems.
Advanced knowledge of MLOps tools (MLflow, Kubeflow, DVC), CI/CD pipelines, and model monitoring.
Strong software engineering skills — clean code, scalable design patterns, performance optimization.
Experience with ML deployment and orchestration using containers (Docker, Kubernetes) and cloud platforms (AWS, GCP, Azure).
Familiarity with model governance, reproducibility, explainability, and fairness in AI.
Ability to conduct and present technical research and apply it to real-world problems.
Prior experience leading cross-functional teams or acting as a technical lead in large AI/ML initiatives.
Exposure to system design, data platform architecture, or real-time inference engines.
Contributions to research papers, patents, or open-source ML projects.
Strong presentation skills and the ability to influence technical and non-technical stakeholders.