Senior MLOps Engineer

About the Role

Key Responsibilities

• Build, deploy, and automate AI and ML pipelines on GCP (Vertex AI, BigQuery, Dataproc, Cloud Functions, GKE).
• Deploy, optimize, and scale Large Language Models (LLMs) and other AI/ML models using Hugging Face Transformers, OpenAI API, Google Gemini, Meta Llama, TensorFlow, PyTorch.
• Design and manage data ingestion, transformation, and processing workflows using Apache Airflow (Cloud Composer), Spark, Databricks, and ETL pipelines.
• Deploy AI/ML models and data services using Docker, Kubernetes (GKE), Helm, and serverless architectures like Cloud Run.
• Automate and manage ML/AI deployments using Terraform and GitLab pipelines.
• Develop scalable, fault-tolerant pipelines to train, deploy, and monitor AI models in production environments.
• Deploy AI models using TensorFlow Serving, TorchServe, FastAPI, Flask, and GCP-native serverless technologies like Cloud Run.
• Implement AI/ML monitoring, drift detection, and performance tracking using MLflow, Prometheus, Grafana, and Vertex AI Model Monitoring.
• Ensure governance, access control, and compliance best practices across AI and ML pipelines.
• Design cloud-native solutions with GCP as the primary platform, leveraging GCP's full AI/ML and data engineering stack.

Requirements

• Bachelor's degree or higher in computer science or a related technical discipline.
• 3+ years of MLOps/DevOps/Data Engineering experience, with expertise in Google Cloud Platform (Vertex AI, Dataproc, BigQuery, Cloud Functions, Cloud Composer, GKE).
• Hands-on experience building AI/ML pipelines and data engineering workflows using Apache Airflow (Cloud Composer), Spark, Databricks, and distributed data processing frameworks.
• Experience working with LLMs and traditional AI/ML models, including fine-tuning, inference optimization, quantization, and serving.
• Proficiency in CI/CD for ML, version control (Git), and workflow orchestration (Airflow, Kubeflow, MLflow).
• Strong experience with Terraform for infrastructure automation.
• Proven ability to build, deploy, and maintain AI models in real-world production environments.
• Programming Skills: Proficiency in Python and familiarity with Bash, Scala, or Terraform scripting.
• Experience with security best practices for ML models, including IAM, data encryption, and model governance.

Nice to Have

• Experience with multi-cloud AI/ML solutions.
• Knowledge of Feature Stores (Feast, Vertex AI Feature Store, AWS Feature Store).
• Understanding of AIOps and ML observability tools.
• Experience with real-time AI inference pipelines and low-latency model serving.
• Gitlab CI/CD with focus on CI/CD for GCP deployments.
• Experience working with PHI/PII in HIPAA and/or GDPR compliant environments.

Qualifications

• Not explicitly specified beyond the degree and experience requirements, but implied: formal education in a technical discipline and relevant professional experience.

Benefits & Perks

• Comprehensive medical, dental, vision, prescription drug coverage.
• Company-provided basic life, accidental death & dismemberment, short-term and long-term disability insurance.
• Tuition reimbursement, student loan assistance.
• A generous 401(k) match.
• Flexible time off, paid holidays, and paid leave programs.
• Other company-provided benefits.