Lead ML Engineer

Job Title: Lead ML Engineer

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About the Role

Lead ML Engineer

Key Responsibilities

🔹 Team Leadership

• Lead and mentor ML engineers and foster high-quality engineering culture.
• Guide team members on ML system design, coding practices, and problem-solving.

🔹 Architecture & Strategy

• Design high-throughput, scalable ML/AI systems.
• Develop the technical roadmap for ML architecture, infrastructure, and product capabilities.

🔹 Model Development & Deployment

• Lead the full lifecycle of ML model development: prototype → production → monitoring → retraining.
• Deploy robust ML pipelines using modern MLOps tools and distributed systems.

🔹 Production ML Ownership

• Ensure operational excellence for ML services in production.
• Implement monitoring, alerting, CI/CD for ML, rollback strategies, and fail-safe designs.
• Manage troubleshooting, performance tuning, and resilience.

🔹 Cross-Functional Collaboration

• Partner with data scientists, software engineers, product teams, and leadership.
• Translate business objectives into ML engineering requirements.

🔹 Innovation & Best Practices

• Evaluate and adopt emerging ML/AI technologies.
• Establish coding standards, design patterns, and governance for ML systems.

🔹 Technical Leadership

• Provide support on high-impact projects.
• Resolve complex technical roadblocks and drive architectural decisions.

Required Skills & Qualifications

• 7+ years of professional experience in ML engineering.
• Strong hands-on experience with

  ML frameworks

  : TensorFlow, PyTorch, Scikit-learn.
• Strong engineering background: Python, Java, C++, Go (any one + Python preferred).
• Experience deploying ML models into production at scale.
• Solid foundation in

  MLOps

  : Docker, Kubernetes, CI/CD, orchestration.
• Deep understanding of

  data pipelines, feature engineering, distributed systems

  .
• Experience with AWS / Azure / GCP ML tools (SageMaker, Vertex AI, Azure ML).
• Strong analytical, architectural, and leadership skills.
• Ability to communicate complex ML concepts to stakeholders.

Good to Have

• Experience building generative AI, vector databases, or LLM pipelines.
• Experience with model experimentation tools (MLflow, Weights & Biases).
• Experience leading ML transformations in enterprise environments.