AI Engineer

AI Engineer

Tampa, FL
 
Requirements:
Extensive Experience: Minimum of 5 years of proven software development experience.
Modern Application Development:
In-depth knowledge of modern application architecture principles.
Clear understanding of Data Structures and Object Oriented Principles
Practical experience with Artificial Intelligence (AI) tools for enhancing development workflows.
Proficiency in Microservices frameworks Event-Driven Services, and Cloud-Native Application Development.
Multiple years of experience on Service Oriented and Microservices architectures, including REST and GraphQL implementations
Full Stack Proficiency: Demonstrated ability to design, develop, and maintain both front-end and back-end components of robust web applications.
Front-End Development: Strong expertise in developing intuitive user interfaces using contemporary JavaScript frameworks (e.g., React), HTML5, and CSS.
Back-End Development: Solid experience in developing server-side logic and APIs using languages Python, Java, or similar.
Database Expertise: Comprehensive knowledge of SQL and PL/SQL, with a deep understanding of Relational Database Management Systems (RDBMS), particularly Oracle.
API Development: Proven capability in designing, developing, and implementing high-performance RESTful APIs leveraging appropriate frameworks and technologies.
CI/CD and DevOps:
Proficiency with Continuous Integration/Continuous Deployment (CI/CD) pipelines and tools for building (e.g., Maven, Gradle) and deploying code (e.g., Docker, Jenkins, OpenShift).
Experience with AWS is considered a significant advantage.
Agile Methodologies: Practical experience working within Agile development methodologies and utilizing project management tools such as JIRA.
Testing Automation: Ability to develop and automate comprehensive unit, integration, and end-to-end tests to ensure code quality.
Version Control: Solid understanding and practical experience with code versioning tools, including GitHub Enterprise.
Strong Python Engineering: Expert-level proficiency in Python and relevant libraries (e.g., FastAPI, Pydantic, PyTorch, HuggingFace Ecosystem).
Experience with LLM-based pipelines: Proven experience in building and deploying applications using Large Language Models.
Knowledge of vector search and embeddings: Hands-on experience with vector databases and developing embedding pipelines.
RAG Concepts: Strong understanding and practical experience with Retrieval-Augmented Generation (RAG) frameworks (e.g., LangChain, LlamaIndex).
GenAI Tuning: Experience with generative AI tuning techniques such as QLORA, LORA, and PEFT.
MCP Experience: Practical experience with Agentic Workflows, and Model Context Protocol (MCP) for enhancing development workflows
NLP Expertise: Strong hands-on experience with NLP techniques such as text classification, summarization, and topic modeling.