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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.