• Be part of the development team; • Conduct requirements gathering, document, design and implement solutions; • Ensure that established requirements are met; • Manage your own tasks autonomously; • Demonstrate proactive problem-solving skills.

• Support the definition of archetypes, templates, coding standards and best practices. • Analyze documentation produced from legacy systems and validate logic for future rewrites. • Contribute .NET API prototypes to validate architecture. • Participate in discussions about microservices, contracts and integrations. • Assess the technical feasibility of approaches suggested by the architecture team. • Support the initial definition of unit tests and technical acceptance criteria. • Ensure consistency and standardization for the subsequent execution/migration phase. • Develop microservices based on the patterns and documentation created during the Discovery phase. • Perform unit testing.

• Act as a Senior Analyst/Developer specializing in legacy environments (COBOL/Mainframe), focusing on technical and functional analysis of existing systems to support a strategic migration program to AWS. • The professional will be responsible for conducting a complete assessment of the current architecture, including analysis of COBOL programs, batch routines, integrations and flows orchestrated via Control-M, producing technical and functional inputs to guide the modernization of the environment. • Play a critical role in identifying dependencies, mapping data flows, understanding business rules embedded in the legacy system, and producing structured documentation of this information, ensuring visibility and supporting decision-making for migration phases. • Work under the client's direct management, with strong interaction with technical and business teams.

• Lead end-to-end delivery of AI agent implementations • Conduct architectural reviews and provide recommendations for optimizing AI platform performance, scalability, and security • Lead the design and deployment of production-grade AI systems as an active contributor writing high-impact code. Expect to live in Python, LangGraph, and LangFuse to turn vision into reality. • Architect sophisticated, stateful, multi-agent workflows using LangGraph. You will build the frameworks that allow autonomous agents to operate with enterprise-level reliability and scale. • Champion AI observability by integrating LangFuse for deep tracing, prompt versioning, and rigorous evaluation. You’ll turn "black box" LLM interactions into transparent, benchmarked performance data. • Define the engineering blueprints for the entire organization. You will establish patterns for RAG architecture, advanced tool-calling, context window optimization, and prompt engineering. • As the internal scout for emerging tech, you will benchmark new LLM providers and orchestration frameworks, ensuring our stack remains at the cutting edge of the agentic revolution.