Lead AI Engineer

Role : Lead AI Engineer

Location Tampa FL (Day 1 onsite)

Duration Fulltime

Requirements:

• Extensive Experience: Minimum of 10 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.