Key Responsibilities : - 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 efficient 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.Qualifications & Key Skills : - Bachelor's/Master's in Computer Science/Engineering, Data Science,



or a related field.- 5+ years of experience in MLOps, LLMOps, ML Engineering, DevOps, or Platform Engineering.- Robust Python and software engineering skills, with CI/CD (Jenkins), cloud (AWS), Docker, and infrastructure-as-code (Terraform).- Hands-on experience deploying, scaling, and monitoring ML models in production.- Practical experience with LLMs, RAG, embeddings, vector databases, LLM evaluation, and AI agents.- Experience with MLflow, Airflow, Kubeflow, or similar MLOps/LLMOps tools.- Experience deploying and operating web applications and APIs in production (familiarity with Django and FastAPI-based services).- Experience with GPU infrastructure and self-hosted LLM serving (vLLM, TGI, or similar); exposure to parameter-efficient fine-tuning (LoRA/QLoRA).- Familiarity with Model Context Protocol (MCP) or similar tool-calling/agent-integration standards.- Proficiency with Git, APIs, testing, automation, and software development best practices.- Understanding of AI governance, security, model risk, data privacy, and responsible AI.- Experience in financial services / investment management will be advantageous. (ref:hirist.tech)

📌 MLOps Engineer - LLMOps (Bengaluru)
🏢 NobleEdge Talent Advisory
📍 Bengaluru