Principal Engineer / Technical Lead

Aretec Inc•,

About The Position

Aretec, Inc. is seeking a hands-on Principal Engineer / Technical Lead to guide the architecture and implementation of a greenfield AI solution within an existing Enterprise Search team. This is an implementation-focused role: the successful candidate will make critical design decisions, write production code, lead integrations, review code, and manage technical risk across the application and AI stack. The role involves collaborating with engineers, product stakeholders, federal partners, and the Client team to deliver practical, secure, maintainable solutions. The position supports a mission-driven environment and emphasizes sound judgment, continuous improvement, clear communication, quality, traceability, and responsible AI use. The position is contingent upon contract award and final customer approval. Applicants must be sole U.S. citizens; dual citizens are not eligible.

Requirements

Deep hands-on software engineering experience across backend services, APIs, and modern web applications.
Demonstrated success leading architecture and complex implementations for enterprise or mission-critical systems.
Strong Python skills and practical experience with AI/LLM application architecture.
Experience with RAG, retrieval systems, agent orchestration, prompt frameworks, evaluation, and responsible-AI guardrails.
Strong system-integration, data-flow, testing, code-review, and technical-risk-management skills.
Ability to explain technical decisions and tradeoffs clearly to engineers, program leaders, and customer stakeholders.
Ability to work effectively within an existing multidisciplinary delivery team.
Must be a sole U.S. citizen; dual citizens are not eligible.

Nice To Haves

Experience delivering technology for federal agencies or other regulated environments.
Experience integrating with enterprise search, document intelligence, identity, or data platforms.
Knowledge of source attribution, model evaluation, human-in-the-loop workflows, and AI safety patterns.
Experience mentoring senior and intermediate engineers across AI, backend, frontend, and data disciplines.

Responsibilities

Own the overall technical architecture for the AI solution, including application, integration, data, and AI design decisions.
Provide hands-on technical leadership in designing, implementing, testing, and reviewing production code.
Lead complex integrations with the Client, enterprise data platforms, authentication services, and supporting APIs.
Define scalable patterns for LLM integration, retrieval, agent orchestration, source traceability, evaluation, and guardrails.
Translate program objectives into an executable engineering roadmap, sequencing work according to value, complexity, cost, access, and risk.
Set engineering standards for security, reliability, maintainability, observability, documentation, and automated testing.
Identify architectural and delivery risks early; recommend mitigations and communicate tradeoffs to technical and program stakeholders.
Mentor engineers, facilitate design reviews, and resolve cross-stack technical issues while remaining engaged in implementation.
Partner with federal stakeholders and the Client team to ensure outputs remain grounded in source material and support, rather than replace, human judgment.
Learn the mission, solution objectives, Client team architecture, standards, security boundary, and delivery practices; build relationships with engineering, program, product, and federal stakeholders.
Validate the target architecture and highest-priority technical risks, contribute production code, and establish practical review, integration, and testing patterns.
Lead major implementation and integration decisions across AI, backend, frontend, and data workstreams; operationalize evaluation, traceability, and guardrail strategies with measurable quality checks.
Own the technical execution path for key AI solution capabilities and drive them from design through validated delivery; mentor the engineering team and establish sustainable practices for architecture decisions, technical debt, and continuous improvement.