Note: The job is a remote job and is open to candidates in USA. TopDog Law is hiring a Staff Software Engineer, Applied AI to build and own internal tools and platforms that support its operations teams. The role sets technical direction across teams, applies AI to practical business problems, and guides engineering, data, and AI investment decisions.
Responsibilities
- Architect and build internal tools and platforms for operations teams, owning them from design through deployment and ongoing operation, including reliability and incident follow-through
- Define service boundaries, integration patterns, and data flow across teams, and write the technical strategy and RFCs that make the direction clear
- Reduce operational toil by retiring or consolidating systems, managing tech debt and migrations, and weighing long-term maintenance cost in every build-versus-buy decision
- Build reliable integrations with legacy and third-party SaaS systems, with security, access control, and audit trails designed in for sensitive data
- Identify where AI belongs in a solution and where it doesn't, and design it as a well-engineered part of the system (retrieval, agents, LLM-powered features, workflow automation)
- Run time-boxed proofs of concept that answer "is this worth doing?" with evidence, then advance successful proofs of concept into production-ready solutions, with evaluation, observability, reliability, and security
- Assess our AI maturity, define a practical roadmap, and track AI cost and ROI so investment decisions rest on data
- Design data models and instrumentation so every product generates clean, usable data for analytics and strategic planning
- Align teams with differing priorities on shared platforms, standards, and tradeoffs. Build consensus through influence and evidence, and drive decisions when consensus cannot be reached
- Write high-quality code and lead through code reviews, design reviews, and engineering standards, using AI coding tools (e.g., Claude Code, GitHub Copilot) to raise the whole team's delivery speed
- Mentor engineers and serve as a trusted technical advisor to stakeholders and leadership, explaining tradeoffs clearly and driving decisions to closure
Skills
- 10+ years of professional software engineering experience, with a track record of designing and shipping reliable, maintainable production systems
- Demonstrated Staff or Principal-level scope: you have led architecture or a major technical initiative across multiple teams with competing priorities, and aligned them on shared platforms or standards
- Hands-on experience building applications with LLMs and generative AI in production, not just experiments. Demonstrate experience operating AI systems at scale, addressing production challenges, and measuring outcomes
- Working experience rolling out agentic systems into production, including evaluation, guardrails, and monitoring
- Strong backend and systems design skills, including APIs, data modeling, cloud infrastructure, and integration with legacy and third-party SaaS systems
- Reliability engineering experience, including SLOs, idempotency, retries, partial failure handling, and data consistency across integrated systems
- Experience building systems that handle sensitive data, with sound security and governance practices such as access control, audit trails, and PII and privileged data handling
- A track record of taking ownership of essential engineering work: tech debt strategy, migrations, and deprecation of systems that are no longer worth their cost
- Experience designing data models and instrumentation that support analytics
- Working knowledge of modern AI application patterns: RAG, embeddings, vector search, prompt, harness, and context engineering, tool use and agents, and evaluation methods
- Ability to weigh build-versus-buy tradeoffs and balance technical options against cost, complexity, and business value, including choosing simpler solutions or stopping projects when the evidence doesn't support them
- Strong communication skills, including explaining AI concepts and tradeoffs to non-technical audiences and driving decisions to closure
- Experience with vector databases and search technologies, whether managed services or extensions to existing databases
- Experience with a modern data stack (e.g., BigQuery, Snowflake, dbt) and building the data pipelines that feed analytics
- Experience building internal tools or platforms for operations teams
- Familiarity with ML fundamentals, fine-tuning, or model evaluation frameworks
- Experience with AI cost management, such as token usage, model selection, and caching
- Background in a regulated or document-heavy industry such as legal or financial services
Benefits
- Medical, dental, and vision insurance
- 401(k) with company match
- HSA
- Life insurance
- Disability coverage
- Paid time off
- Parental leave
Company Overview