No Third pary resume
. Bachelor of Science in Engineering, Computer Science, Data Science, or Mathematics, or a related field
At least Fivr (35 years’ experience in data science, machine learning, or applied AI development and
At least Seven (7) years’ experience in software engineering, architecture, or web development.
Hands on real time experience with:
1. SQL and relational database systems (e.g., PostgreSQL)
2. Fine-tuning small language models or embedding models
3. Contributing to or maintaining open-source software projects
4. Graph databases or graph extensions (e.g., Neo4j, Apache AGE)
5. Designing and implementing multi-agent or task-oriented AI systems
6. Embedding models, vector similarity, re-ranking, and graph retrieval techniques in RAG systems
7. Version control systems (e.g., Git), containerization technologies (e.g., Docker), andservice-oriented architectures
8. Collaborating with large language models (LLMs), including both API-basedintegration and local deployment
9. Validating AI-generated outputs, mitigating hallucinations, and integrating AI toolsinto production service pipelines
B. Ability to:
· Understand data structures, algorithms, and clean coding principles
· Select and apply appropriate techniques (LLM and non-LLM) based on task . Develop and improve testing and evaluation pipelines for AI systems, including use of synthetic data
· Demonstrate proficiency in Python, including the ability to develop production grade backend services, APIs, middleware, and data pipelines.
· Design and implement AI/ML systems that operate effectively on complex, inconsistent, or evolving datasets while balancing accuracy, latency, and cost (token consumption)
· Collaborate with team members to define system architecture, agent workflows, and data pipelines while working in constrained environments, including limited GPU availability and predefined infrastructure
Knowledge of:
· Hybrid cloud environments and distributed system considerations Threading, asynchronous processing, and queues in backend servers
React and Microsoft Teams Toolkit for developing chatbot user interfaces
· Non-llm data analysis techniques for structured, semi-structured, and unstructured data
· Classical natural language processing (NLP) techniques in addition to LLM-base approaches
· Data science and LLM-related libraries in Rust or other performance-oriented programming languages
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