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.
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.