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.
Model Development and Optimization: Design, build, and deploy ML models for classification, regression, NLP, computer vision, or time-series forecasting.
Data Preparation and Feature Engineering: Clean, preprocess, and transform structured and unstructured datasets for training and inference.
Package and deploy models using tools like Docker, Flask/FastAPI, and Kubernetes.
Work with business stakeholders to translate real-world problems into AI/ML use cases.
Stay updated with the latest research, frameworks, and tools in machine learning and AI.
Collaborate with software developers, DevOps, data analysts, and domain experts for end-to-end solution delivery.
Maintain comprehensive documentation for models, experiments, and pipelines.

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.
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)
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.

AI/ML Engineer

Key skills