:

- Build and manage MLOps/LLMOps pipelines for deployment, monitoring, evaluation, and lifecycle management.
- Productionize ML models, LLM applications, RAG pipelines, embeddings, and AI agents.
- Automate CI/CD, testing, deployment, retraining, and model/prompt versioning across the engineering stack (Angular, Django, FastAPI/LangGraph agentic service) using Jenkins.
- Implement monitoring and observability for model performance, data quality, drift, reliability, latency, and cost, including Langfuse for tracing and evaluation of the agentic AI platform.
- Build secure, scalable AI infrastructure using AWS (application infrastructure), Hetzner (GPU/model hosting infrastructure), Docker, and Terraform.
- Develop production-grade APIs, model-serving and inference systems, and data/ML pipelines.
- Support self-hosted model infrastructure on Hetzner GPU instances - model serving (e.g., vLLM or TGI for productive LLM inference) and fine-tuning pipelines (LoRA/QLoRA), including experiment tracking and adapter versioning.
- Partner with data scientists and investment experts to operationalize AI solutions.

📌 MLOps/LLMOps Engineer (India)
🏢 EquBot
📍 India