MLOps/LLMOps Engineer
Coforge · Hyderabad, Telangana, India
Coforge · Hyderabad, Telangana, India
**Mandatory/Good to Have Skills –** MCP, RAG, Agentic AI, MLOps/LLOps, Agentic AI Orchestration, LLM, Python, Cloud (AWS/Azure/GCP) **Experience –** 6+ Years **Location –** Pune, Hyderabad **About the Role-** **MLOps/LLMOps Enginee** r with a strong background in **continual learning, CI/CD, and cloud infrastructur** e, particularly on **Azure, GCP, and AW** S. The ideal candidate will have extensive hands-on experience in **Python and ML librarie** s (Scikit-learn, TensorFlow, PyTorch), and a proven track record in deploying, monitoring, and optimizing machine learning and large language model pipelines **1. MLOps & LLMOps Pipeline Development** - Design, implement, and automate **end-to-end ML/LLM pipelines** with a focus on **continual learning, model retraining, and A/B testing** . - Integrate **CI/CD workflows** for seamless model deployment, versioning, and rollback strategies. **2. Cloud & Infrastructure Expertise** - Strong hands-on experience with **Azure, GCP, and AWS** cloud platforms, including managed services for ML (Azure ML, Sagemaker, Vertex AI). - Proficiency in **Docker, Kubernetes** , and cloud-native architectures for scalable, containerized deployments. **3. ML & LLM Tools & Frameworks** - Expertise in **ML pipeline tools** : MLflow, Airflow, Kubeflow, Sagemaker, Azure ML. - Experience with **LLM tools and frameworks** : LangChain, LlamaIndex, Hugging Face, OpenAI/Azure OpenAI APIs. - Hands-on experience with **vector databases** : Pinecone, Weaviate, Chroma, Qdrant. **4. Monitoring, Optimization & Scalability** - Implement **monitoring and observability** using tools like Prometheus, Grafana, ELK, and Datadog. - Optimize **GPU compute, inference latency, and model serving** for high-performance, scalable architectures. **5. Programming & Collaboration** - Strong **Python** skills and familiarity with ML libraries (Scikit-learn, TensorFlow, PyTorch). - Collaborate with data scientists, engineers, and product teams to deliver robust, production-grade ML/LLM solutions.