Software Engineer AI/ML Ops

About the role

This is a full-time, hybrid opportunity based in Pleasanton, California with a leading enterprise SaaS organization building next-generation AI-driven accounting solutions. The role focuses on AI/ML Ops, leveraging technologies like PySpark, Python, cloud platforms (AWS/GCP/Azure), and modern orchestration tools to build scalable data pipelines and production-grade machine learning systems.

Responsibilities

• 40% Data Engineering (PySpark, ETL pipelines, data integration)
• 30% AI/ML Systems (LLM pipelines, model orchestration, agent frameworks)
• 20% Cloud & Infrastructure (AWS/GCP/Azure, Kubernetes, CI/CD)
• 10% Observability & Optimization (monitoring, tuning, reliability improvements)

Daily Responsibilities:

• 75% Hands On Engineering (pipeline development, ML systems, infrastructure)
• 5% Management Duties
• 20% Team Collaboration (cross-functional work with engineering and business stakeholders)

Qualifications

• 2+ years of experience in software engineering with Python, Java, or Scala
• Hands-on experience building and maintaining data pipelines (ETL/ELT), preferably with PySpark
• Experience with machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn
• Experience deploying and managing ML/LLM pipelines in production environments
• Familiarity with orchestration tools such as Airflow, Kubeflow, MLflow, or Vertex AI
• Understanding of distributed systems and large-scale data processing
• Experience with CI/CD pipelines, infrastructure-as-code, and DevSecOps practices
• Knowledge of observability tools such as Prometheus, Grafana, or New Relic
• Experience with Docker and Kubernetes for containerization and scaling

Desired Skills & Experience

• Experience integrating with data platforms such as Fivetran, Plaid, or similar API-based connectors
• Familiarity with LangChain, LangGraph, or other agentic AI frameworks
• Experience optimizing large-scale data pipelines (CDC, indexing, performance tuning)
• Knowledge of Responsible AI practices including governance, auditability, and cost tracking
• Strong scripting and automation skills (Python, Bash)
• Experience working with cloud-native infrastructure across AWS, GCP, or Azure
• Familiarity with networking, security practices, and system reliability
• Strong analytical and problem-solving skills with a focus on data-driven decision making

Benefits

• Medical, Dental, and Vision Insurance
• Vacation Time
• Stock Options