What are the responsibilities and job description for the Artificial Intelligence Architect position at Atos?
We are looking for an accomplished & hands-on AI Architect to lead the vision, architecture, and implementation of AI-driven capabilities. The role involves defining AI strategy, system design, model lifecycle management, data architecture, and integration of AI components with existing microservices. The architect will guide technical teams, evaluate emerging AI technologies, and ensure scalable, secure, and high-performance AI solutions.
Roles and Responsibility
- Define and own the AI/ML architectural roadmap, system design, and end-to-end implementation strategy.
- Architect and implement AI capabilities such as:
- Generative AI & RAG systems, embeddings, vector search
- Semantic & NLP-based search across large-scale learning content
- Recommendation systems for personalized learning paths
- Conversational AI / Chatbots / AI Tutor
- Content classification, auto-tagging, clustering
- Predictive analytics and insight dashboards
- Establish AI pipelines, MLOps governance, and model lifecycle management (training, deployment, monitoring, feedback loops).
- Integrate AI solutions with Sunbird microservices, backend APIs, and Elasticsearch/vector DB infrastructure.
- Work closely with product leadership to translate requirements into scalable AI solutions.
- Mentor and guide AI developers, data scientists, and engineering teams.
- Ensure performance, observability, security, privacy, and responsible AI compliance.
- Evaluate and integrate next-generation models, cloud services, frameworks, and open-source tools.
- Support solution design and technical architecture documentation.
Required Skills & Experience
- 10 years of software engineering experience with at least 5 years in AI/ML system design & architecture.
- Deep expertise in:
- NLP, Transformer models, LLMs, embeddings, vector search
- PyTorch / TensorFlow / Hugging Face / LangChain / LlamaIndex
- RAG pipelines, semantic search, recommendation systems
- Vector DBs: Milvus / Qdrant / Pinecone / Weaviate
- MLOps (model deployment, CI/CD, monitoring, feature store)
- Strong proficiency with Python, Java or Node. js, API development and microservices.
- Experience with Elasticsearch, distributed databases (Cassandra / Postgres).
- Hands-on experience with Docker, Kubernetes, CI/CD pipelines, cloud services (AWS/Azure/GCP).
- Strong understanding of security, data privacy, responsible AI, and scalability in enterprise environments.
- Experience working with high-traffic, large-scale platforms.