As an AI/ML Engineer, you will develop and implement machine learning models to power data-driven solutions across key business functions. You'll translate complex problems into actionable AI strategies, building models for tasks such as classification, forecasting, natural language processing, and computer vision.
Key Responsibilities
- Design, train, and deploy machine learning models tailored to specific business requirements
- Preprocess and transform diverse datasets, including unstructured data, to support model training
- Select and apply appropriate algorithms based on data patterns and project goals
- Engineer and evaluate features to enhance model performance and generalization
- Monitor models in production for accuracy, latency, and concept drift
- Package models using containerization tools and serve them via REST APIs
- Implement and maintain CI/CD pipelines for machine learning workflows
- Collaborate with data engineers and developers to integrate models into scalable systems
- Prototype AI applications such as recommendation systems, chatbots, or anomaly detection tools
- Document experiments, pipelines, and model behavior to ensure reproducibility
- Stay current with advancements in AI, including large language models and generative systems
- Communicate technical findings to non-technical stakeholders clearly and effectively
Requirements
You should have 3–5 years of direct experience building and deploying machine learning solutions. Proficiency in Python and core ML libraries—including scikit-learn, pandas, NumPy, and TensorFlow or PyTorch—is essential. Experience with model evaluation, hyperparameter optimization, and building inference APIs is required.
Preferred Background
- Experience with MLOps platforms such as MLflow, Airflow, or Kubeflow
- Familiarity with Docker, Kubernetes, and cloud-based ML services (AWS, GCP, or Azure)
- Hands-on work with NLP or computer vision frameworks like Hugging Face or OpenCV
Work Environment
You’ll work in a collaborative, intellectually rigorous setting that values curiosity, clear communication, and continuous learning. The role requires both independent initiative and close coordination with cross-functional teams to deliver end-to-end AI solutions that generate measurable impact.
