AI/ML Engineer

Stack

Responsibilities

• ▹Design, build and deploy ML models for classification, regression, NLP, computer vision or time-series forecasting
• ▹Select algorithms and techniques based on business needs and data characteristics
• ▹Continuously monitor and improve model performance using metrics and feedback loops
• ▹Clean, preprocess and transform structured and unstructured datasets for training and inference
• ▹Engineer and select features to improve accuracy and generalizability
• ▹Collaborate with data engineers to ensure data quality and accessibility
• ▹Package and deploy models using tools like Docker, Flask/FastAPI and Kubernetes
• ▹Implement CI/CD pipelines for ML using MLflow, Airflow or Kubeflow
• ▹Monitor deployed models for drift, latency and performance in production
• ▹Translate real-world problems into AI/ML use cases; prototype and test AI-driven solutions (recommendation engines, chatbots, fraud detection)
• ▹Contribute to proof-of-concept projects and help scale successful models to production
• ▹Stay current with research, frameworks and tools; experiment with cutting-edge models (LLMs, transformers, generative AI) and assess their viability
• ▹Maintain documentation for models, experiments and pipelines; ensure reproducibility, scalability and compliance with data governance policies

Requirements

• ▹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)
• ▹Familiarity with cloud ML services (AWS SageMaker, Azure ML, GCP AI Platform)
• ▹Familiarity with NLP or computer vision frameworks (e.g. Hugging Face, OpenCV)
• ▹Strong analytical skills

Soft skills