AI Engineer( Mlops )
UST (company site, opens in a new tab)4+ yrsBengaluruOn-siteDirect career pageEst. 12–20 LPA
Posted on 8 Sept
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Job description
Structured overview of role & requirements
About This Role
- Build and maintain end-to-end ML pipelines including data ingestion, training, evaluation, packaging, versioning, and deployment.
- Develop and manage CI/CD pipelines using Jenkins for multiple environments with multi-stage gates.
- Deploy and optimize model-serving APIs on AKS with FastAPI and vLLM; implement observability and DevSecOps practices.
Minimum Requirements
- 4+ years of experience in ML/AI engineering or DevOps with hands-on production MLOps pipeline experience.
- Strong experience with Jenkins CI/CD, Azure DevOps, GitOps, and Databricks ML pipelines including Delta Lake and MLflow.
- Proficiency in Python (FastAPI, Pydantic, async), Bash, and YAML/SQL scripting; cloud knowledge of Azure/AWS/GCP and containerization technologies (Docker, Helm, AKS).
- Experience with model serving technologies including FastAPI, Docker, AKS; experience with ONNX/TensorRT optimizations; databases like PostgreSQL and Redis.
Ideal Candidate Profile
- Experienced in building scalable, production-ready MLOps pipelines with strong automation, monitoring and security practices.
- Comfortable working with cloud native tools and services (Azure primarily, but AWS/GCP knowledge is valuable).
- Exposure to large language model (LLM) fine-tuning pipelines, Ray Serve or BentoML, and infrastructure automation is a plus.