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/Google Cloud Platform/Azure), and modern orchestration tools to build scalable data pipelines and production-grade machine learning systems.
This opportunity is ideal for engineers who want to work at the intersection of data engineering, machine learning, and infrastructure. The standout feature of this role is ownership of end-to-end AI data pipelines powering agentic AI systems at scale. The team is looking for someone who thrives in fast-paced environments, enjoys working with cutting-edge AI tooling, and wants to deepen expertise in ML systems, observability, and cloud-native infrastructure. You will gain hands-on experience with large-scale data systems, LLM-based pipelines, and advanced AI orchestration, all while collaborating in a culture that values continuous learning and innovation. Required Skills & Experience 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, Google Cloud Platform, or Azure
Familiarity with networking, security practices, and system reliability
Strong analytical and problem-solving skills with a focus on data-driven decision making What You Will Be Doing Tech Breakdown 40% Data Engineering (PySpark, ETL pipelines, data integration)
30% AI/ML Systems (LLM pipelines, model orchestration, agent frameworks)
20% Cloud & Infrastructure (AWS/Google Cloud Platform/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) The Offer Bonus eligible
You will receive the following benefits:
Medical, Dental, and Vision Insurance
Vacation Time
Stock Options Applicants must be currently authorized to work in the US on a full-time basis now and in the future.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
- Dice Id: 10105282
- Position Id: 882217
- Posted 1 day ago
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