Candidate Profile — Candidate #3
Position: Contextual AI — Member of Technical Staff Data Platform (Mountain View, CA)
Candidate Location: Saratoga, CA
Experience: 6 years experience
Relevant Experience & Education Highlights
Brings deep expertise in multimodal AI including vision language models, Kubernetes deployments, AWS microservices, and cross-team collaboration, strongly aligning with the Data Platform team's focus on scalable services, RAG workflows, and vector databases.
1. Design and implement scalable services, APIs, and databases to support the processing and ingestion of petabytes of information daily
Developed robust cloud-based microservices and infrastructure to handle large-scale data processing and AI workloads.
- Built AI/ML microservices on AWS for a connected car cloud platform, including driver classification, recommendation engines, and eco-driving scores.
- Innovated cloud-edge infrastructure using Zenoh protocol alongside standard microservices components.
- Collaborated with offshore and internal engineering teams to standardize infrastructure for scalable deployments.
2. Build and improve state-of-the-art multimodal LLMs to maximize document understanding performance
Specialized in multimodal agents and vision language models critical for advanced RAG and document processing systems.
- Led research in multimodal agents, closed-loop simulation, benchmarks, and vision language models (VLMs).
- Integrated AI/ML services supporting multimodal data handling in automotive and edge environments.
- Filed 100+ patents in mobility AI, demonstrating innovation in multimodal and agentic systems.
3. Architect and build streaming infrastructure, data orchestration systems, vector databases
Delivered zero-downtime orchestration and deployment strategies using Kubernetes and AWS for high-availability data systems.
- Implemented zero-downtime deployments on AWS using CodeDeploy, EC2 Auto Scaling groups, elastic load balancers, and blue-green strategies.
- Deployed rolling updates and blue/green strategies for Kubernetes clusters on GCP, enabling instant rollback.
- Pivoted ed-tech company to cloud-based online learning, building products for adaptive test preparation with scalable cloud channels.
Requirements & Candidate Alignment
| Contextual AI Requirement | Candidate Qualification |
|---|---|
| Education: At least a Bachelor's degree in Computer Science, Software Engineering, or related field | Ph.D. in Mechanical Engineering from a top research university |
| Experience: Kubernetes services, distributed queuing systems, and streaming infrastructure | Kubernetes expertise with rolling updates, blue/green deployments on GCP clusters; distributed systems via Zenoh protocol |
| Proven ability to diagnose distributed vector databases and design systems for low-latency retrieval of image, text, audio, and video vectors | Vector database experience aligned with multimodal retrieval needs from vision language models and agents |
| Machine Learning: Familiarity with machine learning concepts and frameworks, including dense information retrieval, document understanding/parsing models, and vision language model | Vision language model (VLM) proficiency through multimodal agents, benchmarks, and AI/ML microservices |
| Problem-Solving: Strong problem-solving skills and the ability to work effectively in a fast-paced, collaborative environment | Solved complex deployment challenges achieving zero-downtime via blue-green strategies and infrastructure innovations |
| Communication: Excellent communication and interpersonal skills, with the ability to work closely with cross-functional teams | Collaborated across offshore teams, researchers, engineers, and product specialists on microservices and cloud pivots |
| Total Experience: Extensive hands-on background in data platforms and AI | 6 years spanning AI/ML services, cloud infrastructure, deployments, and product development |
If you would like to discuss this candidate or other critical roles, here is a link to my calendar to schedule a call:
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