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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building and maintaining MCP servers, integrating LLM models, and optimizing AI systems for production environments. Proficient in Python, SQL, and backend software engineering best practices, with a strong focus on collaboration and problem-solving in AI applications.
Highest-signal resume keywords
MCP Server DevelopmentPython ProgrammingSQL ProficiencyLLM IntegrationPrompt Engineering
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
PythonSQLMCP Server DevelopmentLLM IntegrationPrompt EngineeringIntent ClassificationAPI DevelopmentError HandlingTestingData Aggregation
Soft Skills
CollaborationCommunicationProblem-Solving
Tools & Technologies
SnowflakeAnthropicOpenAIOAuthOIDCAWS
Certifications & Qualifications
Bachelor's Degree in Computer ScienceBachelor's Degree in Software EngineeringBachelor's Degree in Artificial IntelligenceBachelor's Degree in Data Science
Industry Keywords
AI SolutionsConversational AIMulti-API OrchestrationEvaluation MethodologiesBrownfield Codebase
Tech Stack
Tools & technologiesAWSCloudPythonSQL
About the role
Key responsibilities & impact- Build and maintain MCP servers, tool and resource definitions, and context management capabilities
- Design and implement intent evaluation, classification, and routing workflows
- Orchestrate resolution into correct API and data-source calls, including 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 for model quality and production performance
- Write SQL against model inputs and outputs for evaluation and monitoring
- Support backend behavior of the chat/MCP interface
- Trace behavior and make safe, incremental changes in an existing partially documented codebase
- Collaborate 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
Requirements
What you’ll need- 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 production backend software engineering best practices, including APIs, async I/O, typing/schema validation, error handling, and testing
- Strong SQL skills with cloud data warehouses; Snowflake preferred
- Hands-on experience building production-grade LLM and agentic AI applications
- Must have personally authored/built an MCP server deployed in production and used by other users or systems
- Experience building and deploying MCP servers, including tool and resource definitions, context management, and multi-step orchestration
- Understanding of MCP transports, including stdio and Streamable HTTP
- Understanding of authentication and authorization patterns for remote servers, including OAuth/OIDC, scoped access, and resource-level access
- Experience integrating hosted LLM APIs such as Anthropic and OpenAI in production
- Experience with prompt engineering, prompt versioning, fallback strategies, and cost/latency optimization
- Experience designing intent classification and routing solutions for natural language applications
- Experience building conversational AI solutions or multi-API orchestration workflows
- Experience developing LLM evaluation methodologies
- Experience working within a partially documented, brownfield Python codebase
- Strong collaboration, communication, and problem-solving skills
- Ability to explain personal technical work in depth under sustained technical questioning
- Strong spoken English (B2+) for live technical discussions with US-based stakeholders
- Experience building production AI applications with Python and LLM technologies
- Hands-on brownfield experience adding significant features to a production Python codebase
- Experience designing and deploying conversational AI or agentic AI solutions
- Strong experience with prompt engineering, LLM integrations, and evaluation methodologies
- Nice-to-have: Hugging Face, GPU acceleration, MLOps, CI/CD, observability, reproducible pipelines, vector databases, RAG, applied NLP, client-facing AI products, and AWS services
Benefits
Comp & perks- Remote work availability
- Contractor model
- Opportunity to work on production-grade LLM applications and agentic AI systems
- Collaborative, lightly-managed team environment
- Work with modern AI, cloud, data, and software engineering technologies
- Learning and ownership opportunities
- AI-native organization focused on meaningful, scalable solutions