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GenAI Expert
Posted 6 days ago by Next Ventures
Negotiable
Undetermined
Undetermined
Paris, France
Summary: The GenAI Expert role involves designing, implementing, and evolving Generative AI assets on a hybrid GCP/Microsoft platform, focusing on scalability and performance. The candidate will ensure the technical consistency and robustness of Generative AI solutions while serving as a reference expert for LLM architectures. Responsibilities include architecture design, governance, and developing resilient systems. The position requires a deep understanding of GCP and Microsoft AI ecosystems, as well as advanced design for high-volume use cases.
Key Responsibilities:
- Designing decoupled, scalable, and secure architectures for Generative AI solutions.
- Implementing AI governance principles and managing technical debt.
- Designing resilient and fault-tolerant architectures and managing non-functional requirements.
- Defining optimized interaction and ingestion models based on business needs.
- Collaborating with development teams to implement continuous evaluation mechanisms.
Key Skills:
- Proven experience with GCP (Agent Platform, BigQuery) and Microsoft AI ecosystems (Copilot, M365).
- Strong programming skills in Python and SQL.
- Experience with frameworks and protocols such as ADK, A2A, MCP.
- Knowledge of orchestration and function calling in modular code.
- Understanding of Gen AI security requirements and observability practices.
Salary (Rate): undetermined
City: Paris
Country: France
Working Arrangements: undetermined
IR35 Status: undetermined
Seniority Level: undetermined
Industry: IT
GenAI Expert
Mission Objective
Under the supervision of the AI System Architect, the main role is to design, implement, and evolve Gen AI assets on a hybrid GCP/Microsoft platform, with a focus on scalability and performance.
The candidate must ensure the technical consistency, scalability, and robustness of Generative AI solutions and act as a reference expert for LLM architectures.
Tasks Include
- Gen AI Architecture:
Designing decoupled, scalable, and secure architectures (RAG, autonomous agents), defining application architecture for LLM environments (DEV, UAT, PROD), and integrating CI/CD through LLMOps. - Augmented Developer:
Proven experience with “Augmented Developer” solutions is required to successfully handle part of the role’s responsibilities. - Governance & Technical Debt:
Implementing AI governance principles and managing technical debt. - Robust Architectures:
Designing resilient and fault-tolerant architectures, writing interface contracts, developing fallback processes, and managing non-functional requirements (latency, security, reliability). - Interaction & Ingestion Models:
Defining optimized models, analyzing business needs, creating conceptual models for vector storage, optimizing LLM token usage, collaborating with development teams, and implementing continuous evaluation mechanisms.
Expected Technical Skills
- GCP: Agent Platform (formerly Vertex AI), BigQuery Data Agents
- Microsoft: Copilot, Copilot Studio, WorkIQ, M365 Copilot Agents
- Programming: Python, SQL
- Frameworks & Protocols: ADK, A2A, MCP
Specific Expertise & Knowledge Expected in GCP, LLMOps & Cognitive Architectures
- Orchestration & Function Calling Expertise:
Proven experience in decoupling business logic to dynamically interact with Information Systems, ensuring modular and reusable code (Python, Go, etc.). - GCP & Microsoft AI Ecosystems:
Deep knowledge of Agent Platform (formerly Vertex AI), Gemini, and equivalent Microsoft platforms. Ability to select the appropriate models based on cost/performance/complexity trade-offs and work within a multi-model cloud environment. - Advanced Design for High Volumes & Complex Use Cases:
- Large-scale vector database management (Vector Search)
- Advanced semantic caching strategies to reduce latency and costs
- Mastery of agent-based architectures (multi-agent systems, planning, reasoning)
- Gen AI Security Requirements:
- Knowledge of prevention strategies against Prompt Injection and Data Poisoning attacks
- Implementation of strict filtering mechanisms (semantic OLS/RLS) to ensure LLMs only return data accessible to the user
- Observability & Monitoring (LLMOps):
Knowledge of best practices for monitoring AI application chains (prompt traceability, response times, token costs) and forwarding logs to enterprise security layers (SIEM).