AI Model Integration & Deployment

• Integrate pre-trained AI/LLM models (OpenAI, Anthropic, Google, Hugging Face, etc.) into applications
  and backend services

• Design and implement APIs, microservices, and scalable model-serving architectures

• Optimize inference performance to improve speed, latency, and cost efficiency

• Build and maintain end-to-end ML pipelines for data processing and model deployment

• Implement observability tools (logging, monitoring, alerts) for AI systems in production

Model Training & Development

• Train, fine-tune, and evaluate machine learning models for specific use cases

• Build custom ML models using TensorFlow, PyTorch, scikit-learn or similar

• Conduct data preprocessing, feature engineering, and dataset augmentation

• Optimize models through hyperparameter tuning and architecture refinement

• Apply MLOps best practices for model lifecycle management

• Conduct experiments and report on performance metrics

Software Engineering

• Write clean, maintainable, well-documented, and production-ready code

• Develop robust data pipelines for training and inference

• Build RESTful / FastAPI-based APIs for model interaction

• Collaborate with backend, frontend, and product teams to integrate AI features

• Implement resilience patterns (error handling, retries, fallbacks)

• Ensure high code quality through testing, code reviews, and CI/CD workflows

Collaboration & Innovation

• Work closely with product and engineering teams to define AI requirements

• Partner with data scientists to operationalize research models

• Stay up to date with the latest AI/ML/LLM research, frameworks, and tools

• Document architectural decisions, model design, and implementation details

• Mentor junior engineers and guide best practices in ML engineering