Overview The Expert Developer – Application Modernization & Generative AI is responsible for designing, developing, and integrating generative AI solutions and agentic architectures into enterprise systems. This role modernizes existing applications by enriching them with intelligent capabilities (LLMs, RAG, AI agents, copilots) to enhance efficiency, automation, and user experience. The expert serves as a technical reference, contributes to solution engineering, and ensures rigorous execution aligned with IT standards. Responsibilities Application Development & Modernization: Design and develop generative AI–powered features within existing applications (intelligent assistants, content processing, automated resolution, etc.). Refactor and modernize components to enable seamless integration of LLMs and AI agents. Implement APIs, microservices, and AI integration modules within existing IT architectures. Collaborate with architects to translate technical requirements into scalable, industrializable solutions. Generative & Agentic AI Integration: Build solutions leveraging models such as OpenAI, Azure OpenAI, Anthropic, or open‑source LLMs. Design and implement agentic systems orchestrating multiple tools, actions, or reasoning steps. Develop RAG pipelines (indexing, vectorization, retrieval) and advanced contextualization mechanisms. Implement guardrails, robust prompting, security policies, and quality control mechanisms. Engineering, Quality & Performance: Produce maintainable, tested, high‑performance code aligned with industry standards. Conduct technical code reviews, propose improvements, and solve complex engineering challenges. Diagnose, optimize, and monitor the performance of AI solutions in production. Contribute to DevOps/MLOps/GenAIOps practices: CI/CD, containerization, observability, continuous model evaluation. Collaboration & Technical Leadership: Work closely with architects, analysts, and business teams to understand use cases; contribute to workshops, demos, and proofs of concept; provide advanced technical support to development teams; document solutions, patterns, and recommended practices. Innovation & Technology Watch: Stay up to date with advancements in generative AI, agentic architectures, RAG, and orchestration frameworks; experiment with new models, tools, and techniques; identify optimization opportunities within the application ecosystem. Technical Skills Generative AI & Agents Strong knowledge of LLMs and frameworks (OpenAI, Azure OpenAI, HuggingFace, Llama, etc.). Experience with agentic frameworks: LangChain, OpenAI Agents, Semantic Kernel, AutoGen, CrewAI. Expertise in RAG pipelines, embeddings, vector stores, hybrid search. Prompt engineering, hallucination mitigation, guardrails. Development & Application Integration Excellent knowledge of Python (mandatory) and ideally another language (C#, Java, TypeScript). Mastery of REST/GraphQL APIs, microservices, cloud integration. Experience with application modernization (refactor, decoupling, APIfication). Good understanding of cloud architectures (Azure, AWS, or GCP). DevOps / MLOps / GenAIOps Containerization (Docker), orchestration (Kubernetes). CI/CD pipelines. Familiarity with MLOps tools (MLflow, Vertex AI, Azure ML) — a plus. Observability, model evaluation, and monitoring. Soft Skills Strong autonomy, initiative, and ability to solve complex problems. Technological curiosity and willingness to stay ahead of trends. Ability to explain AI concepts to developers and stakeholders. Rigor, attention to detail, and quality‑driven mindset. Collaborative spirit and clear communication. Profile 5–10 years of experience in software development. Experience with generative AI, LLMs, or intelligent systems (or proven project‑based experience). Practical experience integrating AI into existing applications or services. Contributions to PoCs, pilots, or AI production projects. Note: Some company-specific or promotional content has been omitted to focus on the role, responsibilities, and qualifications. #J-18808-Ljbffr