Description

You will manage the end-to-end machine learning lifecycle and deploy large-scale AI solutions.

This role is on-site.

Responsibilities

• Develop and optimize ML pipelines for scalable model development, training, and deployment.
• Automate model training, deployment, monitoring, and testing workflows using CI/CD.
• Deploy and fine-tune open-source LLM models including Llama, Mistral, and R1.
• Manage data pipelines to ensure efficient preparation of training data.
• Monitor LLM performance and evaluate metrics such as latency, accuracy, and cost-effectiveness.

Required Skills

• 5+ years of experience in machine learning operations.
• Deep expertise with Azure ML Studio and AWS SageMaker.
• Experience with MLflow, Databricks, and model versioning.
• Proficiency in containerization and orchestration using Docker and Kubernetes.
• Hands-on LLMOps experience with vLLM, LiteLLM, BentoML, Ollama, and Hugging Face.
• Practical knowledge of LangChain, Langfuse, and RAG implementations.
• Strong software engineering skills in Python and ML frameworks like PyTorch or TensorFlow.
• Experience building microservices and data pipelines.
• Competency with Gitlab or Azure DevOps for CI/CD and workflow automation.

Preferred Skills

• Experience developing AI solutions for product classification, data enrichment, and price optimization.

Key Skills

Education

Any Graduate