- Company Name
- Tech Riders
- Job Title
- Tech Lead IA (H/F)
- Job Description
-
**Job Title**
Tech Lead AI (H/F)
**Role Summary**
Lead the technical vision and delivery of AI initiatives across diverse cross‑functional teams. Drive end‑to‑end AI project life cycles, enforce engineering standards, architect scalable solutions, and ensure alignment with compliance, performance, and environmental goals.
**Expectations**
- Minimum 7 years of senior leadership in AI/ML engineering.
- Proven track record managing full AI project pipelines: ideation, POC, productionization.
- Strong governance mindset covering ethical AI, regulations (IA Act, GDPR), and security.
- Hands‑on expertise with cloud (Azure, AWS, GCP) and on‑prem infrastructures, GPU optimization, and generative model selection (e.g., Mistral, Gemini, QWEN, DeepSeek).
**Key Responsibilities**
1. **AI Project Management** – Own the AI project life cycle: concept, experimentation, scaling, and industrialization.
2. **Technical Standards & Best Practices** – Define code quality, performance, security, maintainability, API integration, and cloud deployment guidelines.
3. **Architecture & Modeling** – Evaluate and challenge architectural choices, select and tune LLMs, implement RAG techniques, and ensure models meet performance, compliance, and security benchmarks.
4. **Scalability & Interoperability** – Guarantee robustness, scalability, and seamless integration of AI models with enterprise systems.
5. **Environmental KPIs** – Define and monitor sustainability metrics (energy efficiency, carbon footprint, GPU usage) and recommend optimizations.
6. **Continuous Innovation** – Maintain active watch on AI advancements and propose forward‑looking technical improvements.
7. **Team Coordination** – Mentor developers, data scientists, and architects; facilitate cross‑team collaboration; oversee deployment and integration with client infrastructures via cloud/on‑prem.
8. **Methodological Improvements** – Suggest and supervise technical and methodological refinements to enhance model deployment efficiency.
**Required Skills**
- AI/ML project lifecycle mastery (ideation → production)
- Deep knowledge of AI governance, ethics, IA Act, GDPR, and security controls
- Expertise in AI architecture: API exposure, cloud (Azure, AWS, GCP), on‑prem, and hybrid deployments
- Proficiency with generative AI models (Mistral, Gemini, QWEN, DeepSeek) – evaluation, selection, optimization
- GPU infrastructure knowledge and performance/energy‑usage optimization techniques
- Advanced programming skills in Python and Java; familiarity with version control, testing, CI/CD pipelines
- Strong analytical, problem‑solving, and communication abilities
- Ability to guide and influence multidisciplinary teams
**Required Education & Certifications**
- Bachelor’s or Master’s degree in Computer Science, AI, Machine Learning, Data Science, or related technical field.
- Professional certifications in AI/ML, cloud platforms (Azure, AWS, GCP), or cybersecurity are an advantage.