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Join the team shaping the future of AI at Scale. The Public Sector software engineers (SWEs) create the core product building blocks forward-deployed teams use to develop agentic capabilities that function across multiple domains. SWEs responsibilities include building the systems required to ingest and process federal datasets to support real-time decision-making in contested environments.
Job Responsibility:
Create multi-layered guardrails around agents
Optimize data retrieval for agents
Orchestrate fleets of asynchronous agents
Automatically alert users to deviations in data
Illustrating how an agent reached a decision
Orchestrate feature implementation across the Federal engineering team to ensure architectural consistency
Define technical strategy for agentic guardrails, explainability, and fleet orchestration
Ensure system reliability and performance across multiple security classifications and network types
Mentor engineers in the process of defining requirements with stakeholders and gathering acceptance
Communicate high-level technical trade-offs and implementation strategies to senior government stakeholders and Scale C-Suite members
Influence the long-term product strategy and technical roadmap for the Federal business unit
Consult on the architecture of AI-powered solutions for large-scale federal contracts
Requirements:
Full Stack Development: Proficiency in front-end, back-end development and infrastructure, including experience with modern web development frameworks, programming languages, and databases
Cloud-Native Technologies: Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and experience in developing and deploying applications in a cloud-native environment. Understanding of containerization (e.g., Docker) and container orchestration (e.g., Kubernetes) is a plus
Data Engineering: Knowledge of ETL (Extract, Transform, Load) processes and experience in building data pipelines to integrate and process diverse data sources. Understanding of data modeling, data warehousing, and data governance principles
AI Application Integration: Familiarity with integrating Large Language Models (LLMs) and building agentic workflows. Understanding of prompt engineering, retrieval-augmented generation (RAG), and agent orchestration is beneficial.
Problem Solving: Strong analytical and problem-solving skills to understand complex challenges and devise effective solutions. Ability to think critically, identify root causes, and propose innovative approaches to overcome technical obstacles
Collaboration and Communication: Excellent interpersonal and communication skills to effectively collaborate with cross-functional teams, stakeholders, and customers. Ability to clearly articulate technical concepts to non-technical audiences and foster a collaborative work environment
Adaptability and Learning Agility: Willingness to embrace new technologies, learn new skills, and adapt to defining and evolving project requirements. Ability to quickly grasp and apply new concepts and stay up-to-date with emerging trends in software engineering
Nice to have:
Understanding of containerization (e.g., Docker) and container orchestration (e.g., Kubernetes)
Understanding of prompt engineering, retrieval-augmented generation (RAG), and agent orchestration