AI/ML Ops Engineer

This is a fully remote position, open to applicants in India.

📋 Description

• Design and automate comprehensive ML pipelines for ongoing training and deployment utilizing Kubeflow, MLflow, or AWS SageMaker Pipelines.

• Orchestrate deployments of containerized models on Kubernetes using KServe and Triton Inference Server.

• Set up low-latency inference endpoints along with auto-scaling GPU and CPU clusters.

• Establish the foundations for model tracking and data versioning, incorporating feature stores, model registries, and DVC.

• Develop automated monitoring systems for AI performance and data, focusing on model accuracy decay, data drift, concept drift, and processing latencies.

• Enhance inference environments through the use of ONNX, TensorRT, and quantization techniques.

• Integrate generative AI and LLM operational frameworks with semantic caching, vector database scaling, and prompt validation pipelines.

• Manage machine learning access controls and security profiles, encompassing data segregation, model access tokens, and encryption protocols.

⛳️ Requirements

• 4 to 8 years of experience in core software engineering, DevOps, or data engineering.

• More than 3 years of dedicated experience in building and maintaining MLOps automation infrastructures.

• Certification in AWS Certified Machine Learning - Specialty, Google Cloud Certified Professional Machine Learning Engineer, or Databricks Certified Machine Learning Professional is required.

• Strong technical expertise in Python programming.

• Experience with Docker and Kubernetes for container orchestration.

• Proficiency with PyTorch, TensorFlow, and Hugging Face.

• Advanced SQL skills.

• In-depth understanding of distributed system mechanics.

• Knowledge of GPU resource management constraints.

• Familiarity with Shadow, Canary, and A/B model deployment strategies.

• Understanding of cloud provider API governance.

• Preferred experience in implementing RAG pipelines or fine-tuning open-source LLM layers in a production environment.

• Familiarity with Terraform is preferred.

🏝️ Benefits

• Flexible remote work arrangement.

• Contract employment.