AI/ML Ops Engineer

Required Qualifications

• 6+ years of experience in AIOps, SRE, or infrastructure engineering, including at least 2+ years working on AI/ML/GenAI workloads.
• Proven experience building and deploying GenAI or LLM-based solutions at scale.
• Deep expertise in container orchestration (Kubernetes, KubeRay), GPU provisioning, and cloud-native infrastructure.
• Strong programming skills in Python or Go, and experience working with ML workflows.
• Experience deploying open-source LLMs (e.g., LLaMA, Qwen) or custom fine-tuned models.
• Knowledge of vector databases (e.g., milvus, qdrant, Pinecone) in RAG pipelines.

Responsibilities

• Design and deploy GenAI workloads at scale, including LLMs and multimodal models using container orchestration platforms like Kubernetes, Ray, or KServe.
• Own the development of the AIOps pipeline, including data ingestion, feature engineering, model training, validation, deployment, and monitoring.
• Demonstrated experience with LLMOps, including prompt management, routing, guardrails, and logic fallback mechanisms.
• Size and allocate GPU/TPU resources for use cases such as RAG, chatbots, image/video generation, and LLM fine-tuning or inference.
• Collaborate with research teams to automate LLM pipelines from training to deployment using MLflow, and manage large-scale training jobs using schedulers like Kubeflow or SLURM.
• Engineer highly available and scalable infrastructure across cloud and hybrid platforms (AWS, GCP, Azure).

Good to Have

• Experience with LLM pretraining and fine-tuning.