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 record of solving real-world problems using supervised, unsupervised, or deep learning methods. Technical Skills: Strong knowledge of: • Python and ML libraries (scikit-learn, pandas, NumPy, TensorFlow/PyTorch) • Model evaluation, hyperparameter tuning, and pipeline automation • REST APIs for model serving and integration Familiarity with: • MLOps tools (MLflow, Airflow, DVC, Docker, Kubernetes) • Cloud ML services (AWS SageMaker, Azure ML, GCP AI Platform) • NLP or computer vision frameworks (e.g., Hugging Face, OpenCV) Soft Skills: • Strong analytical and problem-solving abilities. • Excellent communication skills, both verbal and written. • Ability to work independently and within cross-functional teams. • Curiosity, adaptability, and willingness to learn continuously.