MLOps / DevOps Engineer

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.

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).

Good to Have (Preferred)

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)

Work Location: In person