Senior MLOps Engineer
Core
Build, operationalize, and automate end-to-end MLOps pipelines for a hybrid cloud Data & AI foundation, focusing on GenAI/LLM deployment, voicebot pipelines, and secure data handling.
Role type
Senior MLOps Engineer (GenAI/LLM focus)
Builds
Production SageMaker inference endpoints, Amazon Bedrock API integrations, AgentOps/LLMOps frameworks, and real-time STT/TTS voicebot pipelines.
Domain
Telecommunications / Cloud Infrastructure / Generative AI
Deliverable
production ML models
Required skills
Amazon SageMaker, Amazon Bedrock, MLflow, NVIDIA GPU optimization, Terraform/AWS CDK, GitLab CI/CD, Python, PySpark, Docker, Kubernetes/EKS, RAG/vector databases, data tokenization/FPE
Preferred skills
AWS Certified Machine Learning – Specialty, telecom domain AI/ML experience, EMR-on-EKS, Apache Iceberg
Technologies
Amazon SageMaker, Amazon Bedrock, MLflow, GitLab CI/CD, Terraform, AWS CDK, NVIDIA A100/L40S, Apache Spark, Splunk, Amazon CloudWatch
Responsibilities
Build and automate MLOps pipelines using SageMaker Pipelines and MLflow; Design and manage production SageMaker inference endpoints and Bedrock API integrations; Implement AgentOps/LLMOps frameworks for multi-agent orchestration and safety; Operationalize real-time STT/TTS voicebot pipelines; Optimize GPU node pools for SLM/LLM training and inference; Establish automated CI/CD for ML using GitLab CI/CD and IaC; Integrate data de-identification and tokenization into ML pipelines; Set up telemetry, model drift detection, and cost anomaly alerting.
Seniority
Senior, hands-on IC
