• Design AI systems • Build end-to-end AI solutions using machine learning, deep learning, NLP, and generative AI technologies. • Develop LLM-powered applications • Create applications using foundation models, prompt engineering, retrieval-augmented generation, structured outputs, function/tool calling, and agent workflows. • Build agentic AI solutions • Design and implement AI agents that can plan, reason through multi-step tasks, interact with external tools and APIs, retrieve relevant context, and execute actions within controlled business processes. • Develop multi-agent and orchestration workflows • Create orchestrated AI systems where multiple agents or components collaborate to solve complex tasks, with clear control flow, observability, and fallback handling. • Productionize models and AI agents • Deploy, monitor, and maintain AI/ML models and agentic systems in production environments with strong reliability, performance, and safety standards. • Build data and inference pipelines • Develop pipelines for ingestion, preprocessing, vector search, model inference, agent memory/context management. • Improve quality and evaluation • Define offline and online evaluation frameworks for model quality, latency, safety, task completion, agent reliability, and business outcomes. • Optimize performance and cost • Improve model selection, prompt efficiency, agent orchestration, latency, throughput, caching, token usage, and serving efficiency. • Ensure governance and safety • Apply best practices for security, privacy, responsible AI, model risk controls, guardrails, agent permissions, compliance, and human-in-the-loop review where needed.

• Morning meetings with our internal team and clients • Requirements analysis and design conversations are conducted when you have a new project starting. • Working on issues in Jira with your team - building systems, creating CloudFormation/Serverless templates to deploy resources, etc. • Learning best practices used in the NodeJS, JavaScript/TypeScript, and Python communities • Working with your mentor/team lead to further knowledge of AWS Services, tools, or even new languages to address project needs. • You need to be ready to learn quickly!

• Diriger la conception et la mise en œuvre d’initiatives à grande échelle impliquant plusieurs équipes • Identifier, définir et hiérarchiser les problèmes techniques à fort impact • Résoudre des problèmes techniques complexes et ambigus • Agir en tant qu’autorité technique pour les décisions critiques

• Design, develop, and maintain backend services and APIs using .NET Core, C#. • Build responsive, modular migration features using oDATA API. • Own end-to-end delivery: requirements analysis, development, code reviews, unit testing, deployment, and production support. • Design and optimize database models, queries, indexes, and stored procedures to ensure performance and reliability. • Build and deploy cloud-native applications on AWS, applying best practices for scalability, cost, and security. • Implement CI/CD pipelines using GitHub Actions and automate deployments in collaboration with DevOps. • Troubleshoot production issues, perform root-cause analysis, and drive preventive improvements. • Mentor junior engineers and contribute to engineering standards, reusable components, and architectural decisions.