$120K – $140K/yr
Role overview
Job description
- Lead the technical architecture, design, and implementation of enterprise AI and agentic AI solutions within the Government's existing AI environment, emphasizing reuse of approved infrastructure, services, and model endpoints.
- Define scalable, secure, and maintainable software architectures integrating LLMs, specialized AI agents, enterprise data services, APIs, and human-in-the-loop workflows.
- Design and implement multi-agent orchestration frameworks, including Responder–Evaluator–Reporter workflows, model selection, prompt management, tool invocation, and structured output validation.
- Lead the integration of Government-approved frontier AI models through authorized endpoints, ensuring appropriate model configuration, version management, performance evaluation, and controlled execution.
- Architect retrieval-augmented generation (RAG) solutions that integrate authorized enterprise data sources while preserving source attribution, access controls, data lineage, and traceability.
- Guide integration with Palantir, Databricks, Elasticsearch, and other approved Government data services through secure APIs and governed interfaces.
- Design secure AI agent execution frameworks incorporating identity propagation, role-based and purpose-based access controls, bounded tool permissions, approval checkpoints, execution limits, and safe failure mechanisms.
- Develop and review production-quality backend services, APIs, and orchestration components using Python and modern software engineering practices.
- Lead architecture and code reviews, resolve complex integration challenges, and establish engineering standards for maintainability, reliability, security, and performance.
- Design evaluation and testing approaches for LLM and multi-agent applications, including factual accuracy, consistency, explainability, latency, scalability, and computational cost.
- Establish observability, logging, prompt and model version tracking, and auditability across AI workflows, ensuring AI-generated outputs can be traced to supporting sources and reviewed by authorized personnel.
- Collaborate with cybersecurity, DevSecOps, privacy, and Government stakeholders to align system designs with approved deployment pipelines, security controls, authorization requirements, and release processes.
- Support the adaptation of existing AI algorithms, prompts, and software components into Government-approved investigative workflows, documenting technical lineage, modifications, and integration requirements.
- Produce solution architecture documents, system design specifications, interface control documents, data-flow diagrams, technical evaluations, and engineering implementation guidance.
- Support technical demonstrations, prototype evaluations, user acceptance activities, and Government design reviews.
- Provide technical leadership and mentorship to AI engineers and data engineers while maintaining meaningful hands-on involvement in architecture, development, debugging, and integration.
- Evaluate proposed third-party AI tools, frameworks, and software components for technical suitability, security implications, integration complexity, and reuse of existing Government capabilities, coordinating recommendations through established approval processes.
Qualifications:
- Bachelor's degree in Computer Science, Software Engineering, Computer Engineering, Data Science, Artificial Intelligence, or a related technical field, or equivalent relevant professional experience.
- 12+ years of professional experience in software engineering, AI engineering, data engineering, or enterprise application development, including 3+ years in software architecture or technical leadership roles.
- Demonstrated hands-on experience architecting, developing, and deploying enterprise LLM-powered and generative AI applications, including agentic AI workflows, multi-agent orchestration, and retrieval-augmented generation (RAG).
- Experience designing and implementing multi-agent AI systems using frameworks such as LangGraph, LangChain, Semantic Kernel, AutoGen, or equivalent technologies, including Responder–Evaluator–Reporter workflows, model comparison, and AI output verification.
- Experience with LLM prompt engineering, prompt version management, structured generation, tool/function calling, agent execution controls, and human-in-the-loop workflows.
- Strong Python software engineering skills, including RESTful API development, asynchronous programming, modular application design, and modern software development practices.
- Experience designing distributed applications, microservices, or event-driven architectures and integrating AI capabilities with structured and unstructured enterprise data through secure APIs and retrieval architectures.
- Experience implementing secure, permission-aware enterprise data integrations, including authentication, authorization, identity propagation, data governance, source traceability, and auditability.
- Experience designing and evaluating reliable, explainable AI systems, including factual grounding, source citations, hallucination mitigation, model performance evaluation, and responsible human oversight.
- Experience with LLMOps and AI assurance practices, including automated regression testing, prompt-injection defenses, model/prompt version tracking, application observability, and performance monitoring.
- Experience with cloud-native architectures and containerized application deployment using Docker, AWS or Azure, Kubernetes, Terraform, Helm, or comparable technologies.
- Experience implementing CI/CD pipelines, automated testing, version control, and software configuration management to support secure, reliable application delivery.
- Demonstrated ability to translate technical and mission requirements into scalable system architectures, implementation plans, interface specifications, and engineering deliverables.
- Experience leading technical design and code reviews, troubleshooting complex software integrations, mentoring engineers, and providing hands-on technical guidance throughout the software development lifecycle.
- Strong written and verbal communication skills, including experience producing technical documentation, presenting architectural decisions, and supporting technical demonstrations, Government acceptance testing, and operational pilot deployments.
Preferred Qualifications:
- Advanced degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, or a related technical discipline.
- Experience integrating enterprise AI applications with Palantir, Databricks, Elasticsearch, or similar governed data and analytics platforms.
- Experience developing Python backend services using FastAPI, Pydantic, asyncio, or comparable frameworks.
- Experience supporting secure software development and deployment in Federal Government, Department of Defense, Intelligence Community, or Department of Homeland Security environments.
- Familiarity with Government cybersecurity, privacy, accessibility, and software authorization processes.
- Experience adapting existing AI research, algorithms, prototypes, or software components into operational enterprise applications.
- Experience supporting AI-enabled document processing, intelligent search, case management, investigative analytics, or automated report generation.
Benefits: Expression offers competitive salaries and benefits, such as: 401k matching PPO and HDHP medical/dental/vision insurance Education reimbursement Complimentary life insurance Generous PTO and holiday leave Onsite office gym access Commuter Benefits Plan
About this listing
- Source: Hatch IT's official careers site · still listed there on 12 Nov 2026
- Posted 26 Oct 2026
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What they are looking for
Skills & requirements
Qualification
Bachelor's degree in Computer Science, Software Engineering, Computer Engineering, Data Science, Artificial Intelligence, or a related technical field, or equivalent relevant professional experience; Demonstrated hands-on experience architecting, developing, and deploying enterprise LLM-powered and generative AI applications, including agentic AI workflows, multi-agent orchestration, and retrieval-augmented generation (RAG); Experience with LLM prompt engineering, prompt version management, structured generation, tool/function calling, agent execution controls, and human-in-the-loop workflows
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Hiring company
Hatch IT
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