Senior Machine Learning Engineer

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About the Role

Senior Machine Learning Engineer

Key Responsibilities

• ML Model Development:

  Design, build, and optimize machine learning models for various business applications such as predictive analytics, NLP, computer vision, recommendation systems, or anomaly detection.

• Data Engineering:

  Develop robust data ingestion, preprocessing, and feature engineering pipelines using large, complex, and multi-modal datasets.

• Model Deployment & Scalability:

  Deploy ML models to production environments, ensuring low latency, high availability, and scalability (e.g., using cloud services like AWS Sagemaker, GCP AI Platform, or Azure ML).

• Research & Innovation:

  Stay updated with the latest ML and AI advancements, experiment with cutting-edge algorithms, and recommend their applicability to business needs.

• Collaboration:

  Work closely with data scientists, product managers, software engineers, and stakeholders to translate requirements into scalable ML solutions.

• Monitoring & Maintenance:

  Implement monitoring and retraining pipelines to ensure models remain accurate and relevant over time.

• Mentorship:

  Guide junior team members in best practices, code reviews, and project delivery.

Required Skills & Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field (Ph.D. is a plus).
• 7+ years of professional experience in ML engineering or applied machine learning.
• Proficiency in Python (and relevant libraries such as TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy).
• Strong understanding of ML algorithms, deep learning architectures, and statistical modeling.
• Experience with data processing frameworks (Spark, Dask, or equivalent) and SQL/NoSQL databases.
• Hands-on experience deploying ML models to production (REST APIs, microservices, containerization with Docker/Kubernetes).
• Expertise with cloud-based ML platforms (AWS, GCP, or Azure).
• Solid understanding of MLOps principles and tools (MLflow, Kubeflow, Airflow, CI/CD pipelines).
• Strong problem-solving skills with the ability to handle ambiguous requirements.
• Excellent communication and collaboration skills.

Preferred Qualifications

• Experience with NLP, LLMs, or transformer-based architectures.
• Knowledge of reinforcement learning, graph neural networks, or other advanced ML techniques.
• Background in big data analytics and distributed computing.
• Publications, patents, or open-source contributions in the ML/AI field.