Senior ML Engineer

Job Summary

We are seeking a highly experienced Senior Lead Engineer with 5 to 10 years of expertise in advanced AI technologies, automation frameworks, and cloud-native solutions. The ideal candidate will be a hands-on technologist with strong leadership skills, capable of designing, implementing, and mentoring teams on cutting-edge AI-driven workflows and microservice architectures.

Responsibilities

• ML Model Development : Design and implement scalable machine learning models tailored to business needs.
• Implement Retrieval-Augmented Generation (RAG) pipelines for enterprise-grade knowledge systems.
• Model Training & Evaluation : Build robust training pipelines, optimize hyperparameters, and evaluate models using industry-standard metrics.
• Build scalable applications using Python and integrate with Microservices.
• Feature Engineering : Develop and maintain efficient feature extraction and transformation processes to improve model accuracy and performance.
• Ensure seamless cloud-based AI deployments across AWS, Azure, or GCP.
• MLOps : Implement CI/CD pipelines for ML workflows, ensuring reproducibility, scalability, and automation of model lifecycle management.
• Collaborate with cross-functional teams to embed AI into existing enterprise systems.
• Model Deployment : Lead deployment of ML models into production environments, ensuring reliability, monitoring, and continuous improvement.
• Foster a culture of innovation, knowledge-sharing, and continuous improvement.
• Collaboration & Leadership : Partner with cross-functional teams (Data Science, Product, Engineering) to deliver impactful solutions.
• Mentor and guide junior engineers in best practices.
• Innovation & Research : Stay updated with the latest ML frameworks, tools, and research to continuously enhance organizational capabilities.

Required Skills & Expertise

• 5-10 years in ML engineering, with proven track record of production-grade ML solutions.
• Strong proficiency in Python, ML frameworks (TensorFlow, PyTorch, Scikit-learn).
• Hands-on experience with MLOps tools (MLflow, Kubeflow, Airflow, Docker, Kubernetes).
• Solid understanding of cloud platforms (AWS, Azure, GCP) for ML deployment.
• ML Model Development, Training, Evaluation
• Feature Engineering & Data Preprocessing
• Model Deployment & Monitoring
• CI/CD for ML pipelines
• Excellent problem-solving, communication, and leadership abilities.

Qualifications

• Bachelors or Master's degree in Computer Science, Engineering, or related field.
• 5 to 10 years of professional experience in AI, automation, and cloud technologies.
• Track record of delivering enterprise-scale AI solutions.
• Experience in leading technical teams and mentoring junior engineers.

Preferred Qualifications

• Certifications in GCP (e.g., Professional Machine Learning Engineer, Data Engineer).
• Experience in Agile/Scrum methodologies.
• Exposure to MLOps and CI/CD pipelines for ML models.
• Experience working in a product-led company.