AI/ML Engineer

Job Title: AI/ML Engineer Department: IT Services Reports To: IT Project Manager Job Overview: The AI/ML Engineer plays a critical role in designing, developing, and deploying machine learning models and AI-driven solutions to support strategic business initiatives. The role involves collaborating with cross-functional teams, including software engineering, data analytics, product development, and business stakeholders, to drive intelligent automation, data-driven decision-making, and advanced analytics capabilities. The ideal candidate will have 3 to 5 years of experience in AI/ML model development, with a strong foundation in machine learning algorithms, data preprocessing, and deployment pipelines. Experience with Python, TensorFlow/PyTorch, and cloud-based ML services is essential. Responsibilities: 1. Model Development and Optimization Design, build, and deploy ML models for classification, regression, NLP, computer vision, or time-series forecasting. Select appropriate algorithms and techniques based on business needs and data characteristics. Continuously monitor and improve model performance using metrics and feedback loops. 2. Data Preparation and Feature Engineering Clean, preprocess, and transform structured and unstructured datasets for training and inference. Engineer and select relevant features to improve model accuracy and generalizability. Collaborate with data engineers to ensure data quality and accessibility. 3. Model Deployment and MLOps Package and deploy models using tools like Docker, Flask/FastAPI, and Kubernetes. Implement CI/CD pipelines for ML using platforms like MLflow, Airflow, or Kubeflow. Monitor deployed models for drift, latency, and performance in production environments. 4. AI Solutions and Use Case Implementation Work with business stakeholders to translate real-world problems into AI/ML use cases. Prototype and test AI-driven solutions (e.g., recommendation engines, chatbots, fraud detection). Contribute to proof-of-concept projects and assist in scaling successful models to production. 5. Research and Innovation Stay updated with the latest research, frameworks, and tools in machine learning and AI. Experiment with cutting-edge models (e.g., LLMs, transformers, generative AI) and assess their viability. Promote innovation by recommending and implementing modern AI strategies. 6. Cross-functional Collaboration Collaborate with software developers, DevOps, data analysts, and domain experts for end-to-end solution delivery. Translate technical insights into business value through clear documentation and presentations. 7. Documentation and Best Practices Maintain comprehensive documentation for models, experiments, and pipelines. Ensure reproducibility, scalability, and compliance with data governance policies. Requirements: Experience: 3–5 years of hands-on experience in machine learning model development and deployment. Proven track…