Physicist-ML Core and Artificial Intelligence 100% both Backend & Frontend Architect

About the job
We're looking for an AI and ML Architect CTO who has been in the position before. Someone who has built serious ML/AI systems at a large company or a successful startup and knows what it actually takes to go from architecture to production at scale.
What We're Building

· We're building ViXa AI as core engine of CSMS Physicist-ML Core and Artificial Intelligence Platform for Cybersecurity Real-Time Anomaly Detection and Self-Certification by applying following regulator:
o UN R155 – R156 CAN BUS AUTH Cybersecurity Mgmt, ISO 21434 – TARA Road Vehicle Cybersec
o NIST CSF 2.0 Cyber Security Framework
o IEC 62443 OTA Industrial Cybersec
o ISO 27001 Information Security
o GDPR / Data Privacy EU Data Regulation
o DORA EU Regulatory and compliance
o an AI infrastructure that automates due diligence, risk scoring, and compliance for crypto exchanges using a multi-agent architecture.
· We're developing a proprietary AI model that takes a fundamentally different approach to machine learning. Not iterating on existing architectures - building something new from the ground up.
· We need a physicist who can turn theoretical insights into production systems. People who understand why the math works before they write the code.

The Role:
As a Lead / Head of AI to define our AI vision and execute critical machine learning projects. This is a combined role: strategic leadership plus hands-on implementation.
You'll be developing core components of our AI model - hands-on work at the intersection of physics, mathematics, and ML engineering. This isn't research theatre. You'll own significant pieces of our model architecture from mathematical foundations through implementation and optimisation.
Strategy: Set AI/data science vision, roadmap, and standards. Mentor and grow the team. Make high-level decisions on methodology, infrastructure, and priorities.
Execution: Lead model design, development, and deployment. Work with large, complex datasets to deliver actionable insights integrated into the product.

What You'll Actually Do:

Design and implement novel learning approaches informed by physics and mathematics. Take concepts from statistical mechanics, information theory, and dynamic systems - make them work in a production ML system.
Debug why your theoretically sound approach breaks at scale. Fix it. Ship it.
Daily reality: mathematical derivations, performance optimisation. You'll need to be comfortable moving between theory and systems-level engineering within the same afternoon.

Required Qualifications

PhD in Physics or Mathematics (strong mathematical physics background preferred)
7+ years professional ML/AI engineering, data science, or equivalent

Essential:

· A degree or similar in a STEM, Data, AI or a Programming related subject (such as Computer Science, Physics, Mathematics, Artificial Intelligence) is preferable
· Demonstrable experience developing a variety of machine learning models
· Strong grasp of machine learning frameworks (e.g. PyTorch, Tensorflow)
· Knowledge of machine learning architectures, loss functions, tools and techniques
· Experience training machine learning models, including hyperparameter tuning and optimizing model performance
· Experience with (or at least exposure to) MLops workflows
· Experience with Python
· Experience with SQL
· Critical thinking and ability to problem solve
· Experience with data warehousing and database systems
· Exposure to working with CI/CD
· Knowledge of developing data pipelines
· Exposure to ETL (Extraction, Transformation and Load)
· Solid understanding of software engineering principles
· Proven track record of successfully completing AI projects that deliver tangible business results
· Experienced writing production level code
· Experience developing neural networks into production
· Experience with Docker
· Experience with NLP and/or computer vision
· Exposure to cloud technologies (eg. Azure)
· Exposure to Big data technologies
· Exposure to Apache products eg. Hive, Spark, Hadoop, NiFi
· Programming experience in other languages

Core Technical Areas:

· Advanced linear algebra, optimisation, numerical methods
· Probability, statistics, information theory
· Graph theory
· Deep hands-on experience in ML, LLMs, and AI system design
· Previous experience at a well-known company or a startup that shipped real product
· Ability to translate complex AI architecture into a scalable, maintainable codebase
· React o Vue. js, TypeScript; Redux (Toolkit) o Zustand
· CSS3, Styled-Components o Tailwind CSS; WebSockets
· Node. js con Express. js / NestJS; Python (FastAPI)
· RESTful API and GraphQL
· PostgreSQL, MongoDB; Prisma, Sequelize, Mongoose
· Docker, Kubernetes (K8s); Azure
· GitHub Actions / GitLab CI/CD; Gravitee (API Gateway & Auth)
· D3. js, Chart. js / Apache ECharts per dashboard and analytics
· Esposizione a infrastructure-as-code (IaC) e pipeline CI/CD
 

Physics:

· Statistical physics and theoretical physics
· Dynamic systems (energy landscapes, emergent systems)
· CSMS Platform Architecture
· Modelling and simulation

Machine Learning:

· Parameter-free learning approaches
· Bayesian methods and belief systems
· Unsupervised learning and Graph Neural Networks
· Computational optimisation at scale
· Strong foundation in algorithms and data structures

Engineering:
· Python proficiency
· ML frameworks (PyTorch, TensorFlow, or equivalent)
· Distributed training systems
· Translating theory into working implementations
· Knowledge graphs, reinforcement learning, and ontology development

Who Thrives Here:

· You work fast in loosely defined environments. Competing priorities don't slow you down.
· You own problems end-to-end. If you need to learn something to solve it, you learn it.
· You're comfortable with ambiguity and rapid context switching. Startup pace doesn't rattle you.
· Clear communicator in English. Self-sufficient but collaborates well.
· Currently fully remote, but flexible if we need hybrid arrangements later.

This Won't Fit If:

· You need complete specs before starting
· You think rigorous means slow
· Current ML paradigms satisfy you
· Proving theorems appeals more than deploying systems

What We Offer:
· Direct Impact: Build proprietary AI architecture from scratch
· Meaningful equity stake: you're joining as a co-builder, not a hire
· Growth: Lead teams as we scale
· Autonomy: Real ownership of technical decisions
· Flexibility: Fully remote within Vietnam/Thai (UTC hours)
· 2,700 – 3,000 USD Compensation: Competitive salary and equity package (discussed during interviews)

Location:
· Fully remote, Vietnam/Thailand based, available during UTC business hours. May transition to hybrid later.
· 12 months fixed contract: We're reviewing applications on a rolling basis and responding quickly to strong candidates.
· 12 team members. High calibre. Real ownership.
· As we scale, opportunities to build and lead teams.
· We're hiring immediately.