Overview
The Machine Learning Engineering Lead is responsible for leading the design, development, deployment, and scaling of machine learning models and systems. This role bridges data science and engineering, ensuring that models are production-ready, scalable, and aligned with business objectives.
You will lead a team of ML engineers, collaborate with data scientists, and work closely with product and infrastructure teams to deliver end-to-end ML solutions.
Key Responsibilities
- Lead the development and deployment of machine learning models into production environments
- Design scalable ML pipelines, feature engineering workflows, and model serving architectures
- Collaborate with data scientists to operationalize models and improve performance
- Establish MLOps best practices (CI/CD for ML, monitoring, retraining pipelines)
- Optimize model performance, latency, and scalability
- Ensure data quality, model governance, and reproducibility
- Work with cloud platforms (AWS, GCP, Azure) for ML infrastructure
- Mentor and guide ML engineers and junior team members
- Stay updated with the latest advancements in machine learning and AI technologies
Qualifications
Education & Experience:
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, AI, or related field
- 5–10+ years of experience in software engineering, with strong exposure to machine learning systems
- Proven experience leading ML projects or teams
Skills & Competencies:
- Strong programming skills (Python, Java, or Scala)
- Experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn)
- Knowledge of MLOps tools (Kubeflow, MLflow, Airflow)
- Experience with data pipelines and big data tools (Spark, Kafka)
- Understanding of model deployment, APIs, and microservices
- Familiarity with containerization (Docker, Kubernetes)
- Strong problem-solving and leadership skills
Preferred:
- Experience in deep learning, NLP, or recommendation systems
- Exposure to large-scale distributed systems
- Publications or contributions to ML projects
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