Deployed Engineer @ LangChain | Agentic AI • LangGraph • Production LLM Systems • Customer Engineering
Deployed Engineer at LangChain, part of the founding EMEA team working with customers to design, build, evaluate, and adopt practical AI agent systems using LangChain, LangGraph, and LangSmith. 9.8 years delivering practical solutions across pharma, chemicals, materials, manufacturing, and consumer goods.
Background combines AI/ML, software engineering, decision intelligence, and customer-facing delivery. Delivered projects to enterprise clients including Rolls-Royce, BAT, NASA, voestalpine, and FUCHS. Built AI platforms from research through production, with expertise in agentic AI, production LLM systems, and ML for sparse, noisy, high-dimensional industrial data.
Bridge technical depth (PhD, 30+ publications, chartered engineer) with commercial acumen and hands-on implementation. Previously contributed through 40× ARR growth (£50k → £2M) over five years at Intellegens, supporting ~£3M in tracked business opportunity value as technical lead / solutions engineer.
Algorithm Development • Conformal Prediction • Uncertainty Quantification • Bayesian Optimization • Active Learning • Deep Learning • Federated Learning • Adaptive Experimental Design
LangChain/LangGraph • RAG Systems • Multi-Agent Architectures • MCP Protocol
Python (NumPy, Pandas, SciKit-Learn, PyTorch, TensorFlow) • Docker • MCP (Model Context Protocol) • REST APIs • FastAPI • Backend Development • Full-Stack Product Building • SQL • GCP/Vertex AI • Git • CI/CD
Materials Science • Pharmaceuticals • Chemicals • Manufacturing • Formulation Development • Process Safety • Experimental Design (DoE)
Technical Pre-Sales • Enterprise Delivery • Stakeholder Management • C-Suite Communication • Thought Leadership • Training & Workshops
LangChain | London, UK (Hybrid) | June 2026 - Present
Part of the founding EMEA team working with customers to design, build, evaluate, and adopt practical AI agent systems using LangChain, LangGraph, and LangSmith.
Focus Areas: Customer engineering • Agent architecture • Evaluation & observability • Production deployment • Enterprise adoption
Intellegens | Cambridge, UK | May 2024 - June 2026
Leading agentic AI development and enterprise adoption—combining technical innovation with strategic client partnerships across pharma, chemicals, and materials.
Key Achievements:
- Supported ~£3M in tracked business opportunity value as assigned technical lead, ranking first among ML scientists—more than 2× the next-nearest contributor
- Architected agentic AI platform - multi-agent architectures, LangGraph, MCP integration for autonomous R&D workflows
- Led cross-functional team of ML engineers, implementing agile sprints
- Delivered technical demonstrations across pharma, chemicals, manufacturing, food, and materials sectors
- Conducted training and workshops for enterprise clients (voestalpine webinar)
- Delivered webinar "Can Agentic AI Transform Chemicals & Materials R&D?"
- Authored blog series on agentic AI for R&D
- Presented at AIChE Spring Meeting 2024 on ML applications in process safety
Technical Highlights: LangGraph • Multi-Agent Systems • RAG • MCP Protocol • Gemini/LLM Integration • Prompt Engineering • Full-Stack Development
Intellegens | Cambridge, UK | May 2023 - May 2024
Led high-impact ML consultancy for Fortune 500 clients. Built and scaled solutions for sparse, noisy industrial data.
Key Achievements:
- Collaborated with DOW on adaptive experimental design (book chapter)
- Implemented Reinforcement Learning for up to 50% reduction in hyperparameter optimization time
- Established LLM-based Q&A document search framework, improving internal data retrieval
Technical Highlights: Reinforcement Learning • LLMs • Bayesian Methods • Neural Networks
Intellegens | Cambridge, UK | September 2022 - May 2023
Developed production ML systems and pioneered explainable AI tools.
Key Achievements:
- Delivered Yili case study on food formulations optimization
- Conducted AM optimization webinar with Lawrence Livermore National Lab
- Developed edge computing algorithm reducing model size by 14,000x whilst maintaining 70% accuracy
- Innovated TensorFlow model that improved core temperature prediction accuracy by 40%
Technical Highlights: TensorFlow • Edge Computing • Explainable AI • Uncertainty Quantification
Intellegens | Cambridge, UK | July 2021 - September 2022
Early team member post-spin-out from Cambridge's Cavendish Lab. Built foundational ML solutions for enterprise clients.
Key Achievements:
- Contributed through company scale-up from ~£50k to ~£2M ARR over five-year tenure (40× growth)
- Published OCAS case study on steel PSP modeling
- Published AMRC case study on composite manufacturing optimization
- Delivered webinar with Lucideon on materials and process development
- Devised advanced automated data clustering using Bayesian methods and Kullback-Leibler divergence
Technical Highlights: Active Learning • Gaussian Processes • Bayesian Methods • Feature Engineering
University of Leicester | Leicester, UK | September 2016 - June 2021
Doctoral research sponsored by Rolls-Royce plc, applying machine learning and statistical modeling to aerospace materials challenges.
Key Achievements:
- Published 16 peer-reviewed papers (4 as lead author) in Acta Materialia, Scientific Reports, Crystals
- Co-developed DenMap algorithm for automated microstructure recognition, adopted by international groups
- Certified AFHEA through 4 years teaching data analysis and simulation
- Led residential advisor team managing student welfare (2015-2017)
Publications highlight: "On the origin of mosaicity in directionally solidified Ni-base superalloys" (Acta Materialia, 2021)
-
BAT - Pharmacokinetics modeling (paper)
-
Zizo - Pharmacokinetics modeling (paper)
-
B-Secur - Pharmacokinetics modeling (paper)
-
Equivital - Core body temperature prediction (paper)
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DOW - Adaptive experimental design (book chapter)
-
Photocentric - 3D printing materials ML pilot (partnership)
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Ansys - Integration partnership (partnership)
-
PlantSea - Sustainable materials pre-sales (case study)
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Avery Dennison - ML pilot (LinkedIn recommendation)
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FUCHS - Lubricant formulation development (case study)
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voestalpine - Advanced materials and AM optimization (webinar)
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Yili - Food formulation optimization (case study)
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OCAS/ArcelorMittal - Steel PSP modeling (case study)
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AMRC - Composite manufacturing optimization (case study)
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Lawrence Livermore National Lab - AM process optimization (webinar)
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Lucideon - Materials development (webinar)
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CPI - Battery industrialisation (webinar)
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Rolls-Royce - Nickel-base superalloy design (PhD thesis)
Additional enterprise clients across pharma, chemicals, and manufacturing under NDA.
Recent highlights — not exhaustive.
- "Talk Freely, Execute Strictly: Schema-Gated Agentic AI for Flexible and Reproducible Scientific Workflows" - arXiv Preprint, 2026
- "Building
Trustworthy AI Agents for Science: Lessons from NOA, a schema-gated
conversational research testbed" - Intellegens Technical Paper, 2026
- "Know Thy Limits: Calibrated Uncertainty for Safer Rehabilitation" - Journal of Rehabilitation Therapy, 2026
- "Degrees of
Uncertainty: Conformal Deep Learning for Core Body Temperature
Prediction" - Communications Engineering, 2025
- "Rapid
Residual Stress Simulation in Additive Manufacturing through Machine
Learning" - Additive Manufacturing, 2025
-
"Adaptive Experimental Design" - The Digital Transformation of
Product Formulation, 2024
- "Quantifying Benefits of Imputation
over QSAR Methods" - J. Chemical Information & Modeling,
2024
- AIChE Global
Process Safety Conference 2025 - ML in Process Safety
- AIM 2025
Conference - Federated Learning in Materials Science
- User Group
Meeting 2025 - NOA Agentic AI System Demo
- AIChE Spring
Meeting 2024 - ML for Chemicals & Materials R&D
- "Can Agentic AI
Transform Chemicals & Materials R&D?" (2025)
- "Tracking LLMs in Materials Science"
(2024)
- "Revolutionizing
Energy Storage with AI" (2023)
- Multiple formulation development and DoE webinars (2021-2024)
- Targets Up Front for More Focused Adaptive Design
- Machine Learning for Oligonucleotides
- The Benefits of Accelerators for Science
- Federated Learning for Adaptive Design
Total Publications: 30+ papers | Citations: 339+ | Active Research Profile: Ongoing collaborations
PhD in Materials Science with Machine Learning
University of Leicester | September 2016 - June 2021
-
Sponsored by Rolls-Royce plc
- Thesis: "Patterns in Directionally
Solidified Alloys" (algorithmic microstructure analysis)
- Developed
image feature recognition tool (DenMap) for automated microstructure
analysis
- 16 publications during PhD, 4 as first author
- Advanced
training: Solidification Modeling (ESI Group, Switzerland)
MEng (Mechanical) with First Class Honours
University of Leicester | September 2011 - June 2016
Professional Certifications:
- Chartered Engineer
(CEng)
- Professional Member, Institute of Materials, Minerals and
Mining (MIMMM)
- Associate Fellow of Higher Education Academy (AFHEA)
-
Essential Management Skills Certificate
- IBM Data Science
Certificate
Continental Divide Trail (2017) — Hiked 3,100 miles from Mexico to Canada along the Rocky Mountains over 6 months, raising funds for MQ Mental Health and the University of Leicester's Widening Participation scheme. One of approximately 200 annual completions. [University feature | Trail journal]