What are the responsibilities and job description for the Software Engineer AI/ML Ops position at Motion Recruitment?
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.
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
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, 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 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/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) The Offer
- Bonus eligible
- 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.