Artificial Intelligence/Large Language Model Engineer

As an Artificial Intelligence/Large Language Model Engineer at GDIT, you will take on numerous responsibilities that include consulting on AI implementation, evaluating opportunities for AI to enhance scientific processes, and fine-tuning large language models to tailor them for specific business needs. You'll collaborate with scientific users to design AI solutions, ensure adherence to enterprise architecture standards, and provide training for effective AI usage within the organization.

To apply for the Artificial Intelligence/Large Language Model Engineer position at GDIT, you should have a Bachelor of Science degree in Computer Science or a related field, alongside 10+ years of relevant software development experience. Key skills include strong Python programming, experience with NLP libraries, knowledge of LLM fine-tuning, and an understanding of transformer architectures. Familiarity with cloud platforms like AWS or Azure for LLM deployment is also essential.

As an AI Engineer at GDIT, proficiency in Python is essential, particularly for leveraging machine learning frameworks and libraries such as HuggingFace, Transformers, spaCy, and NLTK. Familiarity with tools for model deployment like FastAPI or Flask, as well as containerization technologies like Docker, is critical for your success in this role.

To demonstrate experience in LLM fine-tuning for the GDIT position, you should highlight specific projects where you have fine-tuned open-source language models such as LLaMa or GPT. Discuss the prompts you crafted for effective inference and any metrics you tracked to evaluate model performance and effectiveness in fulfilling business needs.

GDIT offers an attractive benefits package for the Artificial Intelligence/Large Language Model Engineer role, which includes flexible work hours, a comprehensive health and wellness plan, a 401(k) with company matching, and opportunities for professional growth such as paid education and certification programs. GDIT also emphasizes work/life balance, providing ample paid time off and support for your career advancement.

In my career, I have extensively used Python for machine learning projects, particularly in data preprocessing, model training, and deployment. An example of my work includes developing a sentiment analysis model using scikit-learn, where I implemented feature extraction techniques and optimized model parameters to enhance predictive accuracy.

I typically begin fine-tuning LLMs such as GPT by preparing specific training datasets that match the desired use-case. I focus on crafting high-quality prompts that allow the model to generate relevant outputs, and I adjust hyperparameters to minimize validation loss, ensuring that the model learns effectively from the input data.

To assess the effectiveness of an AI solution, I would first establish key performance indicators (KPIs) aligned with the project objectives. After implementing the solution, I would analyze metrics such as precision, recall, and user satisfaction to gauge impact, while also soliciting feedback from end-users to address any improvements.

I have utilized various NLP libraries, including HuggingFace and spaCy, for tasks such as text classification and named entity recognition. In one project, I employed spaCy to preprocess a large corpus of text data and trained a transformer model using HuggingFace to optimize the extraction of key information.

When deploying AI models to a cloud environment, I consider factors such as scalability, resource management, and security. It's crucial to optimize model performance to reduce latency and inference times, and using tools like Docker ensures consistent environments across different platforms, while also taking into account cost-effective resource usage.

I stay current with advancements in AI and machine learning by regularly reading industry journals, attending relevant webinars, and participating in online courses on platforms like Coursera. Engaging with AI communities on forums like GitHub and LinkedIn also helps me understand emerging trends and technologies.

Transformer architectures are crucial for NLP due to their ability to process sequences of data without relying on recurrence. They utilize self-attention mechanisms, which allow the model to weigh the importance of different words in a sentence dynamically, improving context understanding and producing more coherent responses.

In machine learning applications, I begin data preprocessing by cleaning the data to remove noise and inconsistencies. This involves normalization, tokenization, and encoding categorical data. Additionally, I may employ techniques such as stemming and lemmatization to standardize text data and ensure better model interpretability.

In a recent project, we encountered issues with model overfitting. To overcome this, we implemented dropout regularization, adjusted the model architecture by reducing layers, and utilized cross-validation techniques to ensure the model generalizes well to unseen data while retaining accuracy.

Prompt engineering is significant because it directly affects the relevancy and quality of responses generated by language models. By carefully crafting prompts, I can guide the model towards yielding outputs that are most aligned with user expectations and project goals, which in turn enhances the overall functionality of the application.