Enterprise Architect

• BS Degree and a Minimum of 12 years of overall experience in an IT related field (development background preferred)\n
• Must have prior SSA experience\n
• Minimum 5 years of hands-on experience designing, developing, and implementing AI models (Generative AI experience preferred)\n
• Must be able to obtain and maintain a Public Trust. Contract requirement. \n, * Strong foundation in Machine Learning, Deep Learning, and neural networks (GANs, VAEs, Transformers, LLMs).\n
• Proficiency in programming languages, especially Python, and relevant libraries (Scikit-Learn, TensorFlow, PyTorch, Keras).\n
• Expertise in Natural Language Processing (NLP) for text and language generation.\n
• Data management, preprocessing, and feature engineering skills.\n
• Expertise with prompt engineering techniques for guiding AI models.\n
• Experience with cloud-based AI platforms (AWS, Azure).\n
• Knowledge of software development best practices, including CI/CD pipelines, version control, and testing.\n
• Problem-solving, analytical, and critical thinking skills.\n
• Strong communication and collaboration abilities.\n
• Understanding of AI ethics and responsible AI development. \n, * Prior experience with federal or state governments IT projects.\n
• Familiarity with various LLM models available in the market\n
• Working knowledge of Retrieval Augmented Generation (RAG) to enhance LLM responses\n
• Experience integrating GenAI models and services into existing applications using APIs\n

n Leidos is seeking a 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 these key required skills: \n \n Machine Learning, Deep Learning, Neural Networks, Python, Natural Language Processing, Prompt Engineering, and Data Management and Preprocessing.\n \n \nIf this sounds like a mission you want to be a part of, keep reading!\n \n \nTEAM CULTURE\n \n Your passion and values might be a good fit for our teams if you answer “yes” to the following questions:\n \n \n

• Are you looking for a company that puts employees first, with a focus on career, flexibility, and well-being?\n
• Do you enjoy collaborating with colleagues and teammates and believe that the best ideas are fostered in an inclusive environment?\n
• Are you searching for a team with a strong sense of ownership, urgency, and drive for daily mission success?\n
• Are you comfortable with proactive outward communication and technical leadership?\n
• Do you enjoy being a catalyst, solving complex problems, and providing innovative solutions?\n
• Do you have the flexibility, creativity, and resilience to pivot the mission for success?\n
• Do you have the courage to make tough ethical decisions with pride, transparency, and respect?\n

\n \nMENTORSHIP & CAREER GROWTH\n \n Our teams are dedicated to supporting new team members in an environment that celebrates knowledge sharing and mentorship. Experienced team members will be assigned to new hires for one-on-one mentoring, collaborative reviews, and coaching on customer engagement to help each new hire successfully onboard and demonstrate their skills. Projects and tasks are assigned in a way that leverages your strengths and will help you further develop your skillset.\n \n \nPosition Description:\n \n \n

• 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).\n
• 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.\n
• 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).\n
• Build AI pipelines: Creating the infrastructure and workflows for data preparation, model training, deployment, and ongoing monitoring of generative AI systems.\n
• Integration: Incorporating generative AI models into existing software applications and products.\n
• Research and experimentation: Staying up to date with the latest research in generative AI and experimenting with new algorithms and techniques.\n
• Ethical considerations: Understanding and addressing potential biases, misuse, and ethical implications of generative AI.\n
• All other duties as assigned or directed.\n