AI/ML Engineer

Key Responsibilities

• Model Development & Deployment
  • Design, train, and optimize ML/DL models for classification, prediction, NLP, computer vision, and recommendation systems.
  • Deploy ML models into production using MLOps frameworks (Kubeflow, MLflow, SageMaker, Vertex AI, Azure ML).
  • Develop reusable ML components for scalability and automation.
• Data Engineering for ML
  • Work with large-scale datasets for feature extraction, cleaning, and transformation.
  • Implement data pipelines for real-time and batch ML workloads.
  • Ensure data quality, consistency, and lineage across pipelines.
• MLOps & Automation
  • Build end-to-end automated ML lifecycle pipelines (training, testing, deployment, monitoring).
  • Integrate CI/CD practices into ML model deployment.
  • Implement drift detection, continuous learning, and retraining strategies.
• Performance & Optimization
  • Optimize algorithms for speed, accuracy, and cost efficiency.
  • Leverage GPU/TPU environments for high-performance training.
  • Benchmark models and fine-tune hyperparameters for business KPIs.
• Security & Governance
  • Ensure compliance with ethical AI practices and regulatory frameworks.
  • Implement security measures for ML models (adversarial robustness, secure APIs).
  • Collaborate with cybersecurity and governance teams for responsible AI adoption.
• Collaboration & Innovation
  • Work with data scientists, data engineers, and business analysts to align AI solutions with business outcomes.
  • Mentor junior engineers and contribute to best-practice frameworks.
    • Stay updated on emerging AI/ML research, tools, and technologies.