Application Developer @ Atos

Role Overview:-

• The Tech Architect AWS AI (Anthropic) will be responsible for translating AI solution requirements into scalable and secure AWS-native architectures.
• This role combines architectural leadership with hands-on technical depth in GenAI model integration, data pipelines, and deployment using Amazon Bedrock and Claude models.
• The ideal candidate will bridge the gap between strategic AI vision and engineering execution while ensuring alignment with enterprise cloud and security standards.

Key Responsibilities: -

• Design robust, scalable architectures for GenAI use cases using Amazon Bedrock and Anthropic Claude. Lead architectural decisions involving model orchestration, prompt optimization, RAG pipelines, and API integration.
• Define best practices for implementing AI workflows using SageMaker, Lambda, API Gateway, and Step Functions.
• Review and validate implementation approaches with tech leads and developers; ensure alignment with architecture blueprints.
• Contribute to client proposals, solution pitch decks, and technical sections of RFP/RFI responses.
• Ensure AI solutions meet enterprise requirements for security, privacy, compliance, and performance.
• Collaborate with cloud infrastructure, data engineering, and DevOps teams to ensure seamless deployment and monitoring.
• Stay updated on AWS Bedrock advancements, Claude model improvements, and best practices for GenAI governance.

Required Skills and Competencies: -

• Deep hands-on experience with Amazon Bedrock, Claude (Anthropic), Amazon Titan, and embedding-based workflows. Proficient in Python and cloud-native API development; experienced with JSON, RESTful integrations, and serverless orchestration.
• Strong understanding of SageMaker (model training, tuning, pipelines), real-time inference, and deployment strategies.
• Knowledge of RAG architectures, vector search (e.g., OpenSearch, Pinecone), and prompt engineering techniques. Expertise in IAM, encryption, access control, and responsible AI principles for secure AI deployments.
• Ability to create and communicate high-quality architectural diagrams and technical documentation. Desirable Qualifications: AWS Certified Machine Learning Specialty and/or AWS Certified Solutions Architect Professional.
• Familiarity with LangChain, Haystack, Semantic Kernel in AWS context. Experience with enterprise-grade GenAI use cases such as intelligent search, document summarization, conversational AI, and code copilots.
• Exposure to integrating third-party model APIs and services available via AWS Marketplace. Soft Skills: Strong articulation and technical storytelling capabilities for client and executive conversations.
• Proven leadership in cross-functional project environments with globally distributed teams. Analytical mindset with a focus on delivering reliable, maintainable, and performant AI solutions. Self-driven, curious, and continuously exploring innovations in GenAI and AWS services.