Lead Software Engineer - Applied AI ML Lead
On-sitePalo Alto, California, United States
Job Summary
Execute creative software solutions, design, and technical troubleshooting while driving team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality and delivery speed. Own infrastructure capacity optimization by building predictive models for risk identification and right-sizing, then design and productionize GenAI/agentic AI solutions for automation and decision support. Engineer production-grade backend services in Python/Java and manage cloud-native data pipelines, applying MLOps best practices across the full lifecycle including experimentation, CI/CD, and observability. Lead evaluation sessions with external vendors and internal teams to probe architectural designs and drive outcomes-oriented adoption of new technologies.
Required Qualifications
- Formal training or certification on software engineering concepts
- 5+ years applied experience
- Hands-on practical experience delivering system design, application development, testing, and operational stability
- Advanced in one or more programming language(s)
- Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security
- Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
- Proficient in all aspects of the Software Development Life Cycle
- Strong hands-on data engineering stack: Apache Spark (batch optimization, partitioning, shuffle tuning, reliability), Apache Airflow (DAG design, backfills, alerting, operational reliability, CI patterns), and Apache Iceberg (schema evolution, partition specs, snapshots, compaction)
- Proven applied AI/ML and GenAI delivery with measurable impact (RAG, extraction, summarization, ranking/classification, copilots, evaluation) and demonstrated ability to lead across teams and influence technical standards and execution
- Deep distributed systems + production engineering expertise across APIs/microservices, CI/CD, observability, containers/Kubernetes, security, and reliability
- Must be able to lift 50 lbs
Desired Qualifications
- Experience with MCP (Model Context Protocol), Agent Skills, and structured agentic architectures
- Strong practical usage of AI engineering productivity tooling (for example, GitHub Copilot, Claude Code) in enterprise SDLC environments
- Familiarity with VSI and Cloud Foundry contexts
- Advanced Java engineering proficiency in addition to Python
- Expert-level Python for production systems (packaging, dependency management, performance); strong Java proficiency is a plus
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