Enterprise Architect

Leidos•Baltimore, MD

1d•$131,300 - $237,350

About The Position

Leidos is seeking an Enterprise Architect to be part of the mission solution and help lead SSA’s Digital Modernization Strategy. Join one of our high performing teams responsible for building the next-generation enterprise APIs and modern responsive user interfaces, supporting the Social Security Administration (SSA) and their mission to meet the changing needs of the public, positively impacting at least 65 million American lives per month. We are a team of forward-looking professionals in need of a strong candidate with key required skills: Machine Learning, Deep Learning, Neural Networks, Python, Natural Language Processing, Prompt Engineering, and Data Management and Preprocessing. If this sounds like a mission you want to be a part of, keep reading!

Requirements

BS Degree and a Minimum of 12 years of overall experience in an IT related field (development background preferred)
Must have prior SSA experience
Minimum 5 years of hands-on experience designing, developing, and implementing AI models (Generative AI experience preferred)
Must be able to obtain and maintain a Public Trust. Contract requirement.
Strong foundation in Machine Learning, Deep Learning, and neural networks (GANs, VAEs, Transformers, LLMs).
Proficiency in programming languages, especially Python, and relevant libraries (Scikit-Learn, TensorFlow, PyTorch, Keras).
Expertise in Natural Language Processing (NLP) for text and language generation.
Data management, preprocessing, and feature engineering skills.
Expertise with prompt engineering techniques for guiding AI models.
Experience with cloud-based AI platforms (AWS, Azure).
Knowledge of software development best practices, including CI/CD pipelines, version control, and testing.
Problem-solving, analytical, and critical thinking skills.
Strong communication and collaboration abilities.
Understanding of AI ethics and responsible AI development.

Nice To Haves

Prior experience with federal or state governments IT projects.
Familiarity with various LLM models available in the market
Working knowledge of Retrieval Augmented Generation (RAG) to enhance LLM responses
Experience integrating GenAI models and services into existing applications using APIs

Responsibilities

Design and develop generative models: This includes working with architectures like GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and especially transformer-based models (like those behind LLMs).
Fine-tuning and optimization: Adapting pre-trained generative models for specific tasks and datasets, optimizing their performance, efficiency, and the quality of their generated output.
Prompt engineering: Crafting and refining prompts to guide generative models to produce desired outputs, often for specific applications (e.g., content creation, code generation, design).
Build AI pipelines: Creating the infrastructure and workflows for data preparation, model training, deployment, and ongoing monitoring of generative AI systems.
Integration: Incorporating generative AI models into existing software applications and products.
Research and experimentation: Staying up to date with the latest research in generative AI and experimenting with new algorithms and techniques.
Ethical considerations: Understanding and addressing potential biases, misuse, and ethical implications of generative AI.
All other duties as assigned or directed.