DUTIES & RESPONSIBILITIES

• Architect and develop solutions utilizing Large Language Models and/or ML algorithms

• Implement AI and Agentic workflows utilizing libraries such as LangChain and LangGraph, 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, among other criteria.

• Write clean, efficient, and well-documented Python code, utilizing frameworks and libraries like NumPy, 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 timeframes.

• Commit to continuous learning to keep abreast with the latest AI and machine learning technological advancements, applying these to practical applications for business solutions.

EXPERIENCE

• 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, closed 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 TensorFlow, 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

COMPETENCIES/SKILLS

• 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., NumPy, 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.

CERTIFICATIONS/LICENSES

Add certifications/licenses (if any) here.

• Relevant certifications in machine learning, AI, or data science are highly regarded but not mandatory.
• Certifications like the TensorFlow Developer Certificate or AWS Certified Machine Learning - Specialty are examples of desirable qualifications.