AI Engineer

DUTIES & RESPONSIBILITIES

• Architect and develop solutions utilizing Large Language Models and/or ML algorithms
• Implement AI and Agentic workflows utilizing libraries such as Lang Chain, Lang Graph, or llama-index
• Develop proof of concepts for innovative AI applications and lead learning efforts to adopt new AI techniques, focusing on building user‑centric products that align with the overall product vision
• Perform high‑impact evaluation of LLM‑generated output, including but not limited to relevancy, usefulness, correctness, precision/recall, and other criteria
• Write clean, efficient, and well‑documented Python code, utilizing frameworks and libraries like Num Py, Pandas, and Matplotlib
• Apply a strong understanding of NLP for tasks such as text preprocessing, sentiment analysis, clustering, and language generation models
• Ensure AI systems’ security and conduct rigorous model evaluations to maintain robustness and reliability
• Work as part of an Agile delivery team bringing AI‑centric projects from idea conception to execution, ensuring timely delivery within designated time frames
• Commit to continuous learning to keep abreast with the latest AI and machine learning technological advancements, applying these to practical applications for business solutions

• Minimum of 4 years of professional experience in ML/AI, with substantial work focusing on the practical application of these technologies
• At least 1 year of focused experience with Large and/or Small Language models or open‑source
• Proven track record of developing and deploying AI‑driven applications into production, understanding the full lifecycle from development to deployment and maintenance
• Advanced Python skills, with experience using deep‑learning frameworks such as Tensor Flow and PyTorch, and familiarity with NLP frameworks
• Experience in agile development and enterprise SDLC and the ability to deliver complex projects on schedule
• Experience in full‑stack development is an advantage but not required

• Extensive knowledge of generative AI concepts and techniques, Large Language Models, and how to implement them
• Demonstrable knowledge of Agentic Workflows and advanced RAG techniques
• Utilization of libraries such as Langchain and Langgraph for language model integration
• Function Calling, Fine Tuning vs. Prompt Engineering, Evaluation
• Advanced proficiency in Python and familiarity with relevant frameworks and libraries (e.g., Num Py, Pandas, Matplotlib)
• Strong fundamentals in NLP techniques and applications
• Solid understanding of software development practices, including version control with Git, and full‑stack development capabilities
• Deep aptitude for problem‑solving and analytical skills
• Excellent communication skills for effective collaboration and knowledge dissemination
• Commitment to continuous learning and staying updated on industry and technology trends