Senior MLOps Engineer Beja

Senior MLOps Engineer
Beja
Beja, Beja, Portugal

We are seeking an experienced MLOps Engineer with expertise in Google Cloud Platform (GCP) to design, build, and optimize
-
- end AI, ML, and data engineering pipelines. This role involves deploying machine learning models, LLMs, and traditional AI models, as well as managing data processing workflows in a GCP-first environment.

The ideal candidate will have experience working with Google Kubernetes Engine (GKE), Apache Spark, Dataproc, Terraform, Vertex AI, and Airflow (Cloud Composer) to ensure scalable and efficient AI/ML operations. While Amazon Web Services (AWS) experience is a plus, it is not required.

Required Skills & Qualifications

• 4-year degree preferred relevant experience will be considered
• 3+ years of MLOps/Dev
  Ops/Data Engineering experience, with expertise in Google Cloud Platform (Vertex AI, Dataproc, Big
  Query, 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.
• Proficiency in CI/CD for ML, version control (Git), and workflow orchestration (Airflow, Kubeflow, MLflow).
• Strong experience with Terraform for infrastructure automation.
• Strong knowledge of Apigee for deploying, managing, and securing machine learning APIs at scale.
• Production-ready AI/ML solutions: Proven ability to build, deploy, and maintain AI modelsin
  - 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.

Bonus Qualifications/Experience

• Experience with
  - cloud AI/ML solutions.
• Familiarity with AWS AI/ML services (Sage
  Maker, EMR, Lambda, EKS, Dynamo
  DB).
• Experience working with LLMs and traditional AI/ML models, including
  - tuning, inference optimization, quantization, and serving.
• Knowledge of Feature Stores (Feast, Vertex AI Feature Store, AWS Feature Store).
• Understanding of AIOps and ML observability tools.
• Experience with
  - time AI inference pipelines and
  - 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

Responsibilities:

• Build, deploy, and automate AI and ML pipelines on Google Cloud Platform (GCP) using tools such as Vertex AI, Big
  Query, Dataproc, Cloud Functions, and GKE.
• Deploy, optimize, and scale Large Language Models (LLMs) and other AI/ML models using platforms like Hugging Face Transformers, Open
  AI API, Google Gemini, Meta Llama, Tensor
  Flow, and Py
  Torch.
• 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 including Cloud Run.
• Automate and manage ML/AI deployments using Infrastructure as Code tools such as Terraform and CI/CD pipelines with Git
  Hub Actions or Git
  Lab.
• Develop scalable,
  - tolerant ML pipelines to train, deploy, and monitor models in production environments.
• Deploy AI models using Tensor
  Flow Serving, Torch
  Serve, Fast
  API, Flask, and GCP-native serverless technologies like Cloud Run.
• Implement monitoring, drift detection, and performance tracking for AI/ML models using MLflow, Prometheus, Grafana, and Vertex AI Model Monitoring.
• Ensure security, governance, access control, and compliance best practices across AI and ML workflows.
• Design
  - native architectures with GCP as the core platform, utilizing its AI/ML and data engineering tools.

Position Location: Remote from Portugal