Machine Learning AI Engineer

Position Overview: We are seeking a Senior Machine Learning / AI Engineer with expertise in Databricks, MLOps/LLMOps, and cloud-native architecture. The candidate must have recent experience implementing data science solutions in Databricks and be comfortable deploying web applications via containerized workflows (Docker, Kubernetes). This role involves building scalable AI/ML systems, deploying LLMs, and operationalizing models in production.

Key Responsibilities:

• Design, develop, and deploy ML, Deep Learning, and LLM solutions.

• Implement scalable ML and data pipelines in Databricks (PySpark, Delta Lake, MLflow).

• Build automated MLOps pipelines with model tracking, CI/CD, and registry.

• Deploy and operationalize LLMs, including fine-tuning, prompt optimization, and monitoring.

• Architect secure ML/AI systems on Azure, AWS, or GCP.

• Deploy containerized web apps and ML services using Docker, Kubernetes (AKS/EKS/GKE), Azure Container Apps, ECS, integrated with CI/CD (GitHub Actions, Azure DevOps, Jenkins).

• Mentor engineers, enforce best practices, and lead design/architecture reviews.

Required Skills & Experience:

• 5+ years in ML/AI solution development.

• Recent hands-on experience with Databricks, PySpark, Delta Lake, MLflow.

• Experience with LLMs (Hugging Face, LangChain, Azure OpenAI).

• Strong MLOps, CI/CD, and model monitoring experience.

• Proficiency in Python, PyTorch/TensorFlow, FastAPI/Flask.

• Cloud architecture experience: Azure preferred, AWS/GCP acceptable.

• Skilled in Docker, Kubernetes, Helm, Terraform, IaC for deploying ML and web apps.