• Design and implement CI/CD pipelines for ML workflows;
• Automate ML workflows from experimentation through production deployment;
• Build and manage ML workflow orchestration;
• Implement standardized practices for model versioning, reproducibility, and A/B testing;
• Optimize model deployment for performance, scalability, and cost-efficiency;
• Ensure secure, compliant model pipelines that meet data residency requirements;
• Create monitoring solutions that work consistently across both cloud and on-premises deployments;
• Apply LLMOps practices, including evaluation, monitoring, and prompt/version management;
• Design and execute testing frameworks for LLM evaluation and reliability;
• Assist in integrating LLMs with internal applications and systems;
• Collaborate with Data Scientists, Data Engineers, and application teams to deliver end-to-end AI solutions.

• Proficiency with ML frameworks such as TensorFlow, PyTorch, or scikit-learn;
• Strong programming skills in Python;
• Experience with containerization and orchestration tools (Docker, Kubernetes);
• Familiarity with CI/CD tools (e.g., GitLab CI/CD, GitHub Actions);
• Understanding of cloud platforms;
• Hands-on experience with ML workflow orchestration tools (e.g., Apache Airflow,);
• Experience with experiment tracking and observability tools (e.g., MLflow);
• Understanding of LLM deployment challenges (latency, cost optimization, scaling);
• Experience with LLM evaluation techniques and prompt engineering.

Preferred Qualifications

• Experience with LLM fine-tuning, retrieval-augmented generation (RAG), and vector databases;
• Exposure to building APIs for serving ML models (e.g., FastAPI, Flask);
• Develop frontend interfaces for AI/ML applications;
• Build and maintain middleware components to connect frontend applications with ML models;
• Experience working with telecommunications data or industry-specific AI use cases.