About You You are a Senior AI Engineer passionate about building production-grade LLM applications and agentic AI systems. You thrive in collaborative environments where you design, implement, and optimize AI capabilities that power conversational experiences and intelligent workflows. You enjoy solving complex AI challenges, integrating cutting-edge LLM technologies, and continuously improving production systems through experimentation, evaluation, and engineering best practices. You're comfortable making production design decisions autonomously within a small, lightly-managed team, and defending those decisions directly to engineering leadership. Note: This role operates under a contractor model and requires availability to work in the U.S. Eastern Daylight Time (EDT).   You Bring to Applaudo the Following Competencies: Bachelor's Degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, or a related field is desired, or equivalent professional experience. Strong proficiency in Python and software engineering best practices for production backend services (APIs, async I/O, typing/schema validation, error handling, and testing). Strong SQL skills working with cloud data warehouses (Snowflake preferred). Hands-on experience building production-grade LLM and agentic AI applications. Hands-on experience building and deploying MCP servers in production (server-side, consumed by other users or systems), including tool and resource definitions, context management, and multi-step orchestration. Understanding of MCP transports (stdio vs. Streamable HTTP) and authentication/authorization patterns for remote servers (OAuth/OIDC, scoped and resource-level access). Experience integrating hosted LLM APIs such as Anthropic and OpenAI into production environments. Experience with prompt engineering, prompt versioning, fallback strategies, and cost/latency optimization, with direct ownership of these concerns in production. Experience designing intent classification and routing solutions for natural language applications, resolving user intent into the correct API and data-source calls. Experience building conversational AI solutions or multi-API orchestration workflows. Experience developing LLM evaluation methodologies and demonstrated ability to reverse-engineer and work within a partially documented, brownfield Python codebase. Strong collaboration, communication, and problem-solving skills. Demonstrated ability to explain your own technical work in first-person depth under sustained technical questioning, clearly distinguishing what you personally built, what you contributed to, and what you only observed. Strong spoken English (B2+) to discuss technical scenarios live with US-based stakeholders. Nice to Have: Experience deploying, fine-tuning, and evaluating open-source models using Hugging Face. Experience with GPU acceleration and inference optimization. Experience implementing MLOps practices, including CI/CD, observability, and reproducible pipelines. Experience with vector databases and Retrieval-Augmented Generation (RAG). Applied NLP experience. Experience building client-facing AI products. AWS experience (Batch, ECS, Lambda, SQS). You Will Be Accountable for the Following Responsibilities: Build and maintain MCP servers, tool and resource definitions, and context management capabilities. Design and implement intent evaluation, classification, and routing workflows, including orchestrating resolution into the correct API and data-source calls, with aggregation across multiple sources. Integrate Anthropic and OpenAI models into production environments. Manage prompt versioning, fallback strategies, and LLM performance optimization. Develop and maintain evaluation frameworks to validate model quality and production performance. Write SQL against model inputs and outputs to support evaluation and monitoring. Support the backend behavior of the chat / MCP interface. Work within an existing, partially documented codebase, tracing behavior and making safe, incremental changes. Collaborate closely with software engineers and AI specialists to deliver production-ready AI solutions. Participate in architecture discussions and contribute to AI platform design. Continuously improve AI systems through experimentation, optimization, and engineering best practices.