1. Bachelor Degree is REQUIRED
2. 5+ years of experience building and operating production software, data, or machine learning systems, with strong Python and SQL skills.
3. Professional experience with cloud platforms, managed data services, object storage, containers, and distributed workloads.
4. Experience designing and operating scalable data pipelines or distributed processing systems for large and evolving datasets.
5. Hands-on experience applying large language models to real products, including prompt design, structured outputs, tool use, evaluation, and production monitoring.
6. Strong understanding of embeddings, vector retrieval, RAG architecture, model limitations, and techniques for improving answer quality and faithfulness.
7. 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
About the Role We are seeking a high-impact AI/ML Engineer to build intelligent data products that turn complex, high-volume engineering information into trusted, actionable insight. You will work across applied machine learning, generative AI, data platforms, and cloud engineering to deliver production systems used for search, traceability, analytics, and decision support. This role is ideal for an engineer who can move from architecture to implementation to operational ownership, and who enjoys solving ambiguous problems where data quality, scale, and reliability matter.
Skills Required:
Python, SQL, Artificial Intelligence & Expert Systems, Google Cloud Platform, API, Software Testing, Data Analysis
Skills Preferred:
Data/Analytics dashboards, Data Collection, Data Integrity, Java, Data Acquisition, Data Conversion
Experience Required:
Senior Associate Exp: 3 to 5 years experience in relevant field
• 5+ years of experience building and operating production software, data, or machine learning systems, with 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: 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 Preferred:
• 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.
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