AI/ML EngineerOverview / Summary We are seeking a high-impact AI/ML Engineer to build intelligent data products that transform complex, high-volume engineering information into trusted, actionable insights. This role works across applied machine learning, generative AI, data platforms, and cloud engineering to deliver production systems supporting search, traceability, analytics, and decision support.
The ideal candidate can move from architecture through implementation to operational ownership and enjoys solving complex problems where data quality, scale, and reliability are important.
Key Responsibilities - Architect, build, and operate reliable data products that ingest and transform diverse structured and unstructured information at enterprise scale.
- Create resilient orchestration and delivery patterns for batch and near-real-time workloads, including observability, alerting, and operational runbooks.
- Develop production Retrieval-Augmented Generation (RAG) systems that combine semantic retrieval, structured data, and grounded responses for engineering use cases.
- Design agentic AI workflows that decompose complex questions, select appropriate data sources and tools, validate results, and provide explainable answers with citations.
- Develop and evaluate embedding, document-understanding, and multimodal inference workflows while balancing quality, latency, scalability, and cost.
- Lead cloud architecture, containerization, infrastructure-as-code, and CI/CD practices for secure and repeatable deployment across environments.
- Own system reliability from design through production by investigating incidents, profiling performance, eliminating failure modes, and improving capacity planning.
- Deliver analytics experiences and decision-support tools that make complex technical data useful to engineers, program teams, and leadership.
- Establish data quality, lineage, validation, and governance practices to help users understand information sources and assess data reliability.
- Build incremental and restartable processing with checkpointing and recovery strategies to protect data integrity during long-running or partially failed workloads.
Required Qualifications - Bachelor's degree.
- 5+ years of experience building and operating production software, data, or machine learning systems.
- Strong Python and SQL skills.
- Professional experience with cloud platforms, managed data services, object storage, containers, and distributed workloads.
- Experience designing and operating scalable data pipelines or distributed processing systems for large and evolving datasets.
- Hands-on experience applying large language models to real products, including prompt design, structured outputs, tool use, evaluation, and production monitoring.
- Strong understanding of embeddings, vector retrieval, RAG architecture, model limitations, and techniques for improving answer quality and faithfulness.
- Experience with software engineering fundamentals, including testing, code review, version control, CI/CD, observability, and secure development practices.
- Demonstrated ability to diagnose difficult production problems using measurable evidence, experimentation, profiling, and disciplined root-cause analysis.
- Experience with workflow orchestration, job scheduling, or reliable batch execution frameworks.
- Experience with artificial intelligence and expert systems.
- Experience with GCP.
- Experience working with APIs and software testing.
- Experience with data analysis.
Preferred Qualifications - Experience with agentic AI frameworks, tool-using systems, or multi-step reasoning workflows.
- Experience with managed generative AI, model serving, batch inference, or vector database platforms.
- Experience with infrastructure-as-code and automated cloud delivery.
- Experience extracting meaning from complex documents, legacy formats, technical diagrams, or other semi-structured content at scale.
- Experience in automotive, manufacturing, safety-critical, systems engineering, or another technically regulated domain.
- Experience building internal analytics products or developer-facing tools that translate complex data into clear decisions.
- Experience with data and analytics dashboards.
- Experience with data collection, data integrity, data acquisition, or data conversion.
- Experience with Java.
- Certification program.
