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MLOps / DevOps Engineer at iApp Technologies — PB, IN | Apply 2026 | 3ranga
I
MLOps / DevOps Engineer
python bash microservices aws azure gcp docker kubernetes terraform ansible jenkins ci/cd github actions gitlab ci prometheus linux llm mlops airflow
About the Role
We are looking for an MLOps/DevOps Engineer to build, deploy, and operate infrastructure for LLM and AI workloads in production. You will work closely with ML and backend engineers to create reliable environments for training/fine\-tuning, model serving, and GPU\-based compute, ensuring performance, scalability, and high availability.
Key Responsibilities
Design and manage scalable infrastructure for AI/ML workloads (training, fine\-tuning, inference). Deploy, manage, and optimize GPU\-enabled environments (drivers, CUDA runtime readiness, GPU monitoring, scheduling). Build and maintain CI/CD pipelines for backend services (APIs, microservices), and ML/LLM deployments (model versioning, rollout, rollback). Containerize and orchestrate services using Docker and Kubernetes (EKS/GKE/AKS or self\-managed). Implement best practices for MLOps lifecycle: model packaging and artifact management reproducible deployments environment management across dev/stage/prod Set up observability (metrics, logging, alerting, tracing) for infrastructure and model services. Job Overview
Job type Full-time
Work mode On-site
Location PB, IN
Posted 4d ago
Source Scraped
Improve system reliability via SRE practices: incident response, root\-cause analysis, SLAs/SLOs, capacity planning.
Collaborate with ML engineers to productionize LLM workflows (LoRA adapters, inference endpoints, batch jobs).
Optimize cost and performance (autoscaling, efficient GPU utilization, job scheduling, caching). Required Skills \& Qualifications (Must Have)
3–5 years experience in DevOps / Platform Engineering / MLOps role Strong Linux administration and networking fundamentals. Hands\-on experience with Docker and Kubernetes (deployments, services, ingress, scaling). Experience building CI/CD pipelines (GitHub Actions / GitLab CI / Jenkins). Proficiency in scripting/automation using Python (or strong bash \+ ability to work in Python). Cloud experience with AWS / GCP / Azure (compute, networking, IAM, storage). Familiarity with infrastructure automation and configuration management (Terraform/Ansible is a plus). Experience with model serving frameworks: vLLM, Triton Inference Server, TorchServe, Ray Serve. Exposure to ML lifecycle tools: MLflow, Weights \& Biases, DVC. Understanding of LLM fine\-tuning concepts (LoRA/QLoRA) and deployment requirements. Experience working with distributed systems, job schedulers, or workflow orchestration (Argo, Airflow, Prefect). Knowledge of vector databases / RAG pipelines (FAISS, Pinecone, Weaviate, pgvector). Familiarity with GPU performance tuning/monitoring (nvidia\-smi, DCGM, Prometheus exporters).
Experience LLM: 3 years (Required) Ai architecture: 3 years (Required) DevOps engineer: 3 years (Required)