Data Science and ML Ops Engineer in San Francisco, CA (Locals only)

Overview

On Site
$0,00/-
Accepts corp to corp applications
Contract - W2
Contract - Independent
Contract - to 12/31/2025

Skills

Data and Intelligence-Analytics-Artificial Intelligence

Job Details

TECHNOGEN, Inc. is a Proven Leader in providing full IT Services, Software Development and Solutions for 15 years.

TECHNOGEN is a Small & Woman Owned Minority Business with GSA Advantage Certification. We have offices in VA; MD & Offshore development centers in India. We have successfully executed 100+ projects for clients ranging from small business and non-profits to Fortune 50 companies and federal, state and local agencies.


Hi There,

I am Mahmood Bafana Senior Talent Acquisition Specialist with Technogenic, we are looking to hire a Talented Professional with below skill set to work with one of our clients and came across your profile and wondering if you might interested or exploring the job market, if so, Please share me your resume at

Position: Data Science and ML Ops Engineer

Location: San Francisco, CA (Locals only)

Duration: Contract

Description:

  • Tachyon Predictive AI team seeking a hybrid Data Science & ML Ops Engineer to drive the full lifecycle of machine learning solutions-from data exploration and model development to scalable deployment and monitoring. This role bridges the gap between data science model development and production-grade ML Ops Engineering.

Key Responsibilities

  • Develop predictive models using structured/unstructured data across 10+ business lines, driving fraud reduction, operational efficiency, and customer insights.
  • Leverage AutoML tools (e.g., Vertex AI AutoML, H2O Driverless AI) for low-code/no-code model development, documentation automation, and rapid deployment
  • Develop and maintain ML pipelines using tools like MLflow, Kubeflow, or Vertex AI. Automate model training, testing, deployment, and monitoring in cloud environments (e.g., Google Cloud Platform, AWS, Azure).
  • Implement CI/CD workflows for model lifecycle management, including versioning, monitoring, and retraining.
  • Monitor model performance using observability tools and ensure compliance with model governance frameworks (MRM, documentation, explainability)
  • Collaborate with engineering teams to provision containerized environments and support model scoring via low-latency APIs

Minimum Skills Required:

  • Strong proficiency in Python, SQL, and ML libraries (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch).
  • Experience with cloud platforms and containerization (Docker, Kubernetes).
  • Familiarity with data engineering tools (e.g., Airflow, Spark) and ML Ops frameworks.
  • Solid understanding of software engineering principles and DevOps practices.
  • Ability to communicate complex technical concepts to non-technical stakeholders.

Best Regards,

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