Dasa

MLOps, LLMOps Consultant – AI, Innovation

Dasa

full-time

Posted on:

Location Type: Hybrid

Location: São Paulo • Brazil

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Tech Stack

AirflowApacheBigQueryCloudDockerGoogle Cloud PlatformGrafanaKubernetesPythonTerraform

About the role

  • Productization: Convert experimental notebooks into robust, scalable, and auditable production pipelines;
  • ML Architecture: Design and implement the infrastructure required for the full model lifecycle (training, deployment, monitoring, and retraining);
  • Agent Architecture: Design and implement the infrastructure required for the full lifecycle of LLM-based agents (fine-tuning, deployment, monitoring, and retraining);
  • Automation: Implement CI/CD/CT practices for machine learning models and generative AI solutions;
  • Observability: Ensure real-time visibility into model performance (drift detection, latency, cost monitoring);
  • Governance: Standardize versioning of data, models, and code to ensure reproducibility;
  • Innovation in LLMOps: Support the team in operationalizing agents and RAGs, focusing on evaluation tools and tracing;
  • Build and maintain orchestration pipelines for data and models using tools such as Airflow (Composer) and Kubeflow;
  • Develop machine learning models and AI agents, selecting the best architectures to solve business problems;
  • Manage model deployment (REST APIs, batch, streaming) in Kubernetes or serverless environments;
  • Configure experiment tracking tools;
  • Work on cloud cost optimization related to AI workloads;
  • Collaborate with Data Scientists to refactor code for performance and software engineering best practices;
  • Implement monitoring for model and data quality in production.

Requirements

  • Bachelor's degree in Data Science, Software Engineering, Computer Engineering, or related fields;
  • Programming and engineering: Advanced proficiency in Python and software engineering best practices (SOLID, unit testing, Clean Code);
  • Containerization: Full mastery of Docker and orchestration with Kubernetes (GKE);
  • Cloud (GCP focus): Hands-on experience with Vertex AI (Pipelines, Endpoints), Cloud Build, Artifact Registry, GCS, and IAM;
  • Pipelines and orchestration: Solid experience with Apache Airflow (Cloud Composer) or Kubeflow Pipelines;
  • CI/CD: Building automation pipelines using GitHub Actions, GitLab CI, or Cloud Build;
  • MLOps core: Experience with model tracking and versioning tools (MLflow, DVC, or Vertex AI Model Registry);
  • Infrastructure as Code (IaC): Knowledge of Terraform.
  • ⭐ Differentials:
  • Experience with LLMOps: Running LangChain or LangGraph pipelines using Vertex AI Pipelines or Cloud Run infrastructure;
  • RAG at Google scale: Implementing RAG (Retrieval-Augmented Generation) architectures integrating BigQuery Vector Search or Vertex AI Search;
  • Feature Store: Experience implementing or using Feature Stores;
  • Databases: Knowledge of BigQuery and vector databases (Vector DBs) for semantic search applications;
  • Monitoring: Use of tools such as Grafana or dedicated monitoring stacks;
  • Experience in mission-critical environments: Previous experience in sectors such as healthcare, finance, or insurance, handling governance and data privacy.
Benefits
  • Meal support: Meal Voucher / Food Allowance or on-site cafeteria (depending on location);
  • Health support: Health insurance and life insurance;
  • Professional development: Universidade Dasa, Development and Career Cycle, Technology Academies/PMAX, and the "Programa Crescer" within Dasa;
  • Other: Transportation voucher and performance-based bonus (PPR).
  • 💰 Unique to Dasa – Health Program based on five pillars:
  • Spiritual: yoga;
  • Physical: TotalPass, primary care clinic, discounts on tests and vaccinations;
  • Intellectual: Universidade Dasa;
  • Relational: UAU perks club and SESC benefits;
  • Emotional: Telepsychology.
  • *Benefits may vary depending on the job location and brand.
Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools
PythonDockerKubernetesCI/CDMLOpsTerraformApache AirflowKubeflowMLflowDVC
Soft Skills
collaborationinnovationproblem-solvingcommunicationperformance optimization
Certifications
Bachelor's degree in Data ScienceBachelor's degree in Software EngineeringBachelor's degree in Computer Engineering