AI Engineer - Agentic Systems & LLMs
Team: Engineering
Location: Remote, USA
Commitment: Full-Time
Workplace Type: remote
Responsibilities
- Design and implement production-grade LLM and agentic systems that ingest operational, doctrinal, and policy data and transform it into structured tasks, workflows, and resource-aware plans.
- Build orchestration layers across workflows, tools, and human approvals to support a recommendation-first AI copilot with progressive, user-authorized agentic actions.
- Implement RAG pipelines over doctrine, regulations, and historical data to enable constraint-aware reasoning, sequencing logic, and transparent, explainable recommendations.
- Design trust, permissioning, and rollback mechanisms that adapt system autonomy based on user behavior, experience, and operational risk.
- Instrument systems for auditability, traceability, and user trust, ensuring all outputs can be explained and linked back to source inputs.
- Optimize AI systems for latency, cost, reliability, and explainability in real-world operational environments.
- Collaborate closely with product, UX, engineering, and domain experts to translate complex operational workflows into scalable, human-centered AI systems.
Requirements
- 5-10 years' relevant experience in AI/LLM
- Strong hands-on experience with developing and deploying LLM-based systems in production
- Experience building multi-step agentic AI systems
- Experience in MLOps, automating the process of training and deploying models and supportive architecture
- Must have experience with containerization approaches such as Docker and Kubernetes
- Developed CI/CD pipelines for testing and automation
- Experience in developing or utilizing third party solutions to evaluate and quantify model performance
- Proficiency in Python
- Experience with: LLM orchestration, RAG architectures, Function/tool calling, State management across AI workflows, Vector Databases
- Ability to reason about constraints, optimization, and dependencies
- Comfort operating in ambiguous, fast-moving product environments
Strongly Preferred
- Experience with planning, scheduling, or optimization systems
- Experience with policy-driven or regulated environments
- Familiarity with trust modeling, human-in-the-loop systems, or safety rails
- Prior work on systems where AI actions had real-world consequences
- Willingness to engage deeply with users and iterate based on behavior, not theory
Compensation
- Salary ($130,000 - $180,000) + Equity
- Salary is determined by the applicant's experience, knowledge, skills, abilities, internal equity, and alignment with market data.
Benefits
- Medical, Dental, Vision coverage
- HSA/FSA options
- Parental Leave
- 401(k): 100% match for the first 6% contributed
- Unlimited Paid Time Off
- Home Office Stipend
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