MLOps Engineer

Kanini Software Solutions; Denver, Northern (CO, KY); Hybrid; USD 120,000 - 190,000; Full time

Job summary

Kanini Software Solutions is seeking an MLOps Engineer (GCP Specialization) to design, implement, and maintain scalable ML infrastructure on Google Cloud Platform. You will bridge data science and data engineering to ensure reliable, scalable ML systems aligned with business goals.

Key responsibilities include building deployment pipelines, managing GCP infrastructure, and automating ML workflows with CI/CD.

Qualifications

• Proficiency in programming languages such as Python.
• Expertise with GCP services including Vertex AI, GKE, Cloud Run, BigQuery, Cloud Storage, Cloud Composer.
• Experience with infrastructure-as-code like Terraform.
• Familiarity with containerization (Docker, GKE) and CI/CD pipelines with GitLab/Bitbucket.
• Knowledge of ML frameworks (TensorFlow, PyTorch, scikit-learn) and MLOps tools compatible with GCP.

Responsibilities

• Model deployment: design pipelines for deploying ML models into production on GCP services.
• Infrastructure management: build and maintain scalable GCP-based infrastructure for training, deployment, and inference.
• Automation: develop workflows for data ingestion, model training, validation, and deployment with GCP tools and CI/CD pipelines.
• Monitoring and maintenance: implement monitoring to track performance, drift, and health; take corrective actions.

Skills

Python

Data science collaboration

Problem solving

Tools

GKE

Vertex AI

Cloud Run

Terraform

Docker

Cloud Composer

MLflow / Kubeflow

Job description

The MLOps Engineer (GCP Specialization) is responsiblefor designing, implementing, and maintaining infrastructure and processes onGoogle Cloud Platform (GCP) to enable the seamless development, deployment, andmonitoring of machine learning models at scale. This role bridges data scienceand data engineering, Infrastructure, ensuring that machine learning systemsare reliable, scalable, and optimized for GCP environments.

Key Responsibilities

• Model Deployment: Design and implement pipelines fordeploying machine learning models into production using GCP services suchas AI Platform, Vertex AI, or Cloud Run, Cloud Composer ensuring highavailability and performance.
  
• Infrastructure Management: Build and maintain scalableGCP-based infrastructure using services like Google Compute Engine, GoogleKubernetes Engine (GKE), and Cloud Storage to support model training,deployment, and inference.
  
• Automation: Develop automated workflows for dataingestion, model training, validation, and deployment using GCP tools likeCloud Composer, and CI/CD pipelines integrated with GitLab and BitbucketRepositories.
  
• Monitoring and Maintenance: Implement monitoringsolutions using Google Cloud Monitoring and Logging to track modelperformance, data drift, and system health, and take corrective actions asneeded.
  
• Collaboration: Work closely with data scientists, Dataengineers, Infrastructure and DevOps teams to streamline the ML lifecycleand ensure alignment with business objectives.
  
• Versioning and Reproducibility: Manage versioning ofdatasets, models, and code using GCP tools like Artifact Registry or CloudStorage to ensure reproducibility and traceability of machine learningexperiments.
  
• Optimization: Optimize model performance and resourceutilization on GCP, leveraging containerization with Docker and GKE, andutilizing cost-efficient resources like preemptible VMs or Cloud TPU/GPU.
  
• Security and Compliance: Ensure ML systems comply withdata privacy regulations (e.g., GDPR, CCPA) using GCP’s security toolslike Cloud IAM, VPC Service Controls, and Data Loss Prevention (DLP).
  
• Tooling: Integrate GCP-native tools (e.g., Vertex AI,Cloud composer) and open-source MLOps frameworks (e.g., MLflow, Kubeflow)to support the ML lifecycle.
  

Qualifications

Technical Skills:

• Proficiency in programming languages such as Python.
  
• Expertise in GCP services, including Vertex AI, GoogleKubernetes Engine (GKE), Cloud Run, BigQuery, Cloud Storage, and CloudComposer, Data proc or PySpark and managed Airflow.
  
• Experience with infrastructure-as-code - Terraform.
  
• Familiarity with containerization (Docker, GKE) andCI/CD pipelines, GitLab and Bitbucket.
  
• Knowledge of ML frameworks (TensorFlow, PyTorch,scikit-learn) and MLOps tools compatible with GCP (MLflow, Kubeflow) andGen AI RAG applications.
  
• Understanding of data engineering concepts, includingETL pipelines with BigQuery and Dataflow, Dataproc - Pyspark.
  

Soft Skills:

• Strong problem-solving and analytical skills.
  
• Excellent communication and collaboration abilities.
  
• Ability to work in a fast-paced, cross-functionalenvironment.
  

Preferred Qualifications

• Experience with large-scale distributed ML systems onGCP, such as Vertex AI Pipelines or Kubeflow on GKE, Feature Store.
  
• Exposure to Generative AI (GenAI) andRetrieval-Augmented Generation (RAG) applications and deploymentstrategies.
  
• Familiarity with GCP’s model monitoring tools andtechniques for detecting data drift or model degradation.
  
• Knowledge of microservices architecture and APIdevelopment using Cloud Endpoints or Cloud Functions.
  
• Google Cloud Professional certifications (e.g.,Professional Machine Learning Engineer, Professional Cloud Architect)
  

Kanini Software Solutions, Inc. does not discriminate in employment matters on the basis of race, gender, religion, age, national origin, citizenship, veteran status, family status, disability status, or any other protected class. We support workplace diversity. If you have a disability, please let us know if there is anything we can do to improve the interview process for you; we’re happy to accommodate. Kanini Software Solutions, Inc., 25 Century Blvd., Ste. 602, Nashville, TN 37214.