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. Originally posted on Himalayas