- Company Name
- QUANTHRONEX
- Job Title
- TECHNICAL CO-FOUNDER / CHIEF TECHNOLOGY OFFICER (CTO)
- Job Description
-
**Job Title:**
Technical Co‑Founder / Chief Technology Officer (CTO)
**Role Summary:**
Founding CTO responsible for end‑to‑end technical vision and execution of a patented deep‑tech platform across energy, finance, and advanced computing. Lead engineering hiring, architecture, MVP delivery, technical strategy through seed and post‑seed funding, and representation to investors, partners, and customers.
**Expectations:**
- Build and launch MVP and customer proof‑of‑concepts.
- Assemble, mentor, and grow the founding engineering team.
- Define and enforce engineering standards, processes, and culture.
- Drive technical strategy from seed stage to scale.
- Serve as primary technical spokesperson for investors, partners, and customers.
**Key Responsibilities:**
- Architect core platform, translating patent to production system.
- Produce first‑customer MVP and validate with early partners.
- Oversee all stages of ML/AI development: data ingestion, model design, training, deployment, monitoring.
- Develop scalable, real‑time backend in Python, C++/Rust.
- Design and maintain APIs (REST, WebSocket, gRPC) with OAuth2/mTLS security.
- Lead MLOps pipelines: versioning, CUDA/GPU orchestration, model serving.
- Build real‑time streaming solutions (streaming ingestion, event‑driven architecture, sub‑100 ms latency).
- Conduct technical sales conversations, demos, POCs, and pilot architectures.
- Ensure compliance with regulated industry security and data requirements.
- Translate patent claims and academic research into production-ready systems.
- Mentor and grow engineering talent; maintain 50 %+ coding time.
**Required Skills:**
- **ML/AI & Causal AI:** 7‑10 yrs production ML (PyTorch/TensorFlow), causal inference, Bayesian methods, probabilistic graphical models, optimization (convex, gradient, meta‑heuristics), validation, benchmarking.
- **MLOps:** End‑to‑end model lifecycle, GPU orchestration, cloud‑native deployment.
- **Domain Expertise (preferred):** Energy, finance, telecom, transportation, scientific computing forecasting or simulation, physics‑informed ML, GNNs, neural ODEs.
- **Tools:** DoWhy, CausalML, EconML, or equivalent.
- **Platform & Cloud Architecture:** 8+ yrs backend Python (NumPy, SciPy, Pandas) with performance‑critical C++/Rust modules. Experience with AWS or GCP multi‑region architectures, IaC (Terraform/Pulumi), Kubernetes, real‑time telemetry (100k+ streams).
- **API & Security:** REST, WebSocket, gRPC, OAuth2, mTLS.
- **Customer‑Facing:** MVP rapid iteration, technical sales, demos, POCs. Regulated industry compliance (energy, finance, telco).
- **IP Execution:** Translate patents and research into working systems; perform gap analysis and build validation pipelines.
- **Leadership & Execution:** Proven engineering team leadership; hands‑on coding > 50 % of time.
**Required Education & Certifications:**
- PhD or Master’s in Computer Science, Machine Learning, Statistics, or related field (or equivalent experience).
- Cloud certifications (AWS Certified Solutions Architect, GCP Professional Cloud Architect, or Kubernetes Certified Administrator) highly valued.