Resume
I'm an AI research engineer and manager based in Houston, working at the intersection of generative AI, large language and vision-language models, agentic systems, and physics-inspired learning. I currently lead AI strategy and technical research at BKO AI — designing agentic swarm architectures, GraphRAG pipelines, and multimodal systems for industrial applications. Before that I was an AI Research Scientist at Shell and a Ph.D. researcher at NCSU on the James Webb Space Telescope's EIGER Collaboration.
Generative AILLMsVLMsMultimodal AIEmbodied IntelligenceAI AgentsPhysics AIAI for Science
Experience
BKO AI — Houston, TX
Oct 2024 – Present
Strategic Leadership
Senior AI Manager Jan 2026 – Present
AI Technology Manager May 2025 – Jan 2026
- Executive Strategy: Define and execute the strategic AI roadmap, aligning GenAI, multi-modal, and Knowledge Graph initiatives with business goals to drive innovation and operational efficiency.
- Product Leadership: Direct the lifecycle of all Agentic AI products on the enterprise platform, including Deep-search, Wide-search, Unified Vision, GUI automation, and Self-executing task agents.
- Team Management: Manage a cross-functional team of AI researchers, software engineers, and graph specialists; improved project delivery timelines by 30% through Agile implementation and roadmap alignment.
- MLOps Governance: Established standardized MLOps practices including CI/CD pipelines, model monitoring, and secure Azure-based deployment, ensuring reproducibility and governance.
Technical Core
Principal AI Research Engineer Oct 2024 – Present
- Agentic Swarm Architecture: Architected an AI agentic swarm system integrated within the Knowledge Graph Explorer, enabling autonomous cross-collaboration between agents for complex query resolution.
- GraphRAG Implementation: Spearheaded the design of fine-tuned multi-LLM-agents using GraphRAG and Neo4j on Azure. Streamlined CAD blueprint retrieval and budget estimation, improving planning accuracy by 55% and reducing retrieval latency by 40%.
- Strategic Knowledge Graphs: Designed Context Graphs for Tag-mapping and Failure Mode and Effects Analysis (FMEA), significantly enhancing agent decision-making capabilities and trading signal tracking.
- Multi-Modal Pipeline: Led the development of a pipeline integrating thermal sensors, IoT devices, and text reports for predictive maintenance. Achieved 92% accuracy, reducing equipment downtime by 40% and preventing $15M in annual losses.
Shell International Exploration and Production — Houston, TX
Oct 2022 – Oct 2024
AI Research Scientist Oct 2022 – Oct 2024
- Legal AI Automation: Revolutionized contract generation by fine-tuning LLaMA-3.2 with LoRA, RAG, and LlamaParse. Reduced processing time from 2 weeks to 10 minutes, saving $8M annually. Implemented RLHF and an internal reflection mechanism to ensure legal accuracy.
- 3D Vision at Scale: Developed state-of-the-art multiclass segmentation algorithms using transformer-based LVMs for 10 TB of 4K-resolution CT-scan data. Achieved 0.841 mIoU on severe class imbalances, generating $20M in annual revenue growth.
- Generative Super-Resolution: Applied diffusion models for rock image super-resolution (up to 32K), accelerating processing from 3 days to 15 minutes (99% efficiency boost).
North Carolina State University — Raleigh, NC
Aug 2019 – Oct 2022
Ph.D. Research Assistant Aug 2019 – Oct 2022
- JWST Data Analysis: Built deep learning pipelines to analyze multidimensional spectral data from the James Webb Space Telescope (EIGER Collaboration).
- Bayesian Inference: Reduced measurement errors by 80% in intergalactic medium studies using generative deep learning, achieving reconstruction error rates as low as 0.025%.
- GUI Development: Created deep learning pipelines and GUI software to analyze multidimensional spectral data from the James Webb Space Telescope, a $10B project exploring the deep Universe, using PyTorch.
Education
Ph.D. in Physics
2022 · North Carolina State University, Raleigh, NCConcentration: AI in Physics, Generative Modeling
M.S. in Physics
2018 · North Carolina State University, Raleigh, NC
M.S. in Electrical Engineering
2018 · North Carolina State University, Raleigh, NCConcentration: Computational Intelligence
B.S. in Mechanical Engineering
2015 · North Carolina State University, Raleigh, NC
B.S. in Physics
2015 · North Carolina State University, Raleigh, NC
Skills
Languages & Web
PythonC++C#JavaTypeScriptJavaScriptSQLHTML/CSSMATLAB
Frameworks & Libraries
PyTorchTensorFlowJAXHuggingFaceDiffusersTransformersTRLFastAPIReactViteCUDAScikit-Learn
Agentic AI & Graph
LangChainLangGraphLlamaIndexAgentScopeNeo4jGraphRAG
Cloud & MLOps
AWS (SageMaker, EC2)Azure (AI Studio, DevOps)DockerKubernetesCI/CDGit
Generative AI Tools
ComfyUIGradioGPT-SoVITSOllaman8nDify
Selected projects
- E-Commerce Generative AI Suite · 2022 – Present — StableDiffusionXL, Flux.1, ControlNet, and SegmentAnything2 composed into a controllable 8K product-image pipeline serving 50+ clients.
- Virtual Influencer Pipeline · 2022 – Present — GPT-SoVITS + Kling AI system for multilingual virtual influencers, reducing video customization time to 6 hours.
- Digital Twin with Gaussian Splatting · 2023 – 2024 — Sneaker digital twin reconstruction using NeRF and 3D Gaussian Splatting at 97% geometric accuracy.
- Autonomous Navigation System · 2020 – 2022 — NVIDIA Jetson, LIDAR, and ZED cameras with vSLAM for 0.05 s end-to-end navigation latency.
Awards & publications
Awards
- Top 10 Breakthroughs of the Year · 2023 — EIGER Collaboration for the James Webb Space Telescope
Publications
- B. Liu, R. Bordoloi. A Deep Learning Approach to Quasar Continuum Prediction. Monthly Notices of the Royal Astronomical Society, Vol. 502, Issue 3, April 2021, pp. 3510–3532.
- B. Liu. Ph.D. Thesis: Study of Intergalactic Medium Using Spectroscopy and Photometry with Deep Learning.
- R. Bordoloi, B. Liu. rbcodes v0.2: JWST/NIRCam Grism Spectroscopic Analysis API. Zenodo, October 2022.
- R. Bordoloi, R. A. Simcoe, J. Matthee, D. Kashino, R. Mackenzie, S. J. Lilly, A.-C. Eilers, B. Liu, D. DePalma, M. Yue, R. P. Naidu. EIGER IV. The Cool 10⁴ K Circumgalactic Environment of High-redshift Galaxies Reveals Remarkably Efficient Intergalactic Medium Enrichment. The American Astronomical Society, Vol. 963, Number 1, p. 28, 2024.
- B. Greig, S. E. I. Bosman, F. Davies, D. Durovcikova, H. Fathivavsari, B. Liu, R. A. Meyer, Z. Sun, V. D'Odorico, S. Gallerani, A. Mesinger, Y.-S. Ting. Blind Quasar Reconstruction Challenge: Exploring Methods to Reconstruct the Lyman-Alpha Emission Line of Quasars. Monthly Notices of the Royal Astronomical Society, Vol. 533, Issue 3, September 2024, pp. 3312–3343.
- D. Kashino, S. J. Lilly, J. Matthee, R. Mackenzie, A.-C. Eilers, R. Bordoloi, R. A. Simcoe, R. P. Naidu, M. Yue, B. Liu. EIGER VII. The Evolving Relationship between Galaxies and the Intergalactic Medium in the Final Stages of Reionization. The Astrophysical Journal, Vol. 997, Issue 2, id. 280, 26 pp.
Invited talks
- November 2025 · Houston, TX Grounding AI Agents with Root Cause Analysis beyond MCP Tools — Future AI
- November 2025 · Houston, TX Failure Mode and Effects Analysis Graph as MCP Server for AI Agents — Industrial AI Nexus
- September 2025 · Houston, TX Industrial AI Agents and Agent-as-a-Service — Houston Data and AI
- June 2025 · Houston, TX Empowering AI Agents with MCP and A2A Protocols — Houston Data and AI
- May 2025 · Houston, TX AI Agents and Future — Houston Data and AI
- December 2024 · Houston, TX STORM AI and UI Agent Systems — Chevron Corporation — Athena Search Seminar
- October 2024 · Houston, TX Digital Rock Smart Segmentation 2.0 — Shell International E&P — Knowledge Sharing Session
- April 2024 · Houston, TX 8K-Resolution Segmenting Every Grain — Shell International E&P — Digital Rock Modeling Session
- June 2023 · Houston, TX Generative Diffusion Models in Action — Shell International E&P — Generative-AI Reading Group
- June 2021 · Virtual Explore the Epoch of Reionization Using Deep Learning — Statistical Challenges in Modern Astronomy VII
- June 2020 · Virtual A Deep Learning Approach to Quasar Continuum Prediction — 236th American Astronomical Society Meeting