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.
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.
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
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