AI Architect

• Design, build, and evolve agentic AI systems that reason, plan, execute, and adapt in production environments.
• Take AI-driven features from concept to production in a true 0–1 product environment.
• Write and review high-quality production code (Python-first) across AI pipelines, inference services, orchestration layers, and supporting systems.
• Implement prompt engineering, tool use, memory, evaluation, and guardrails as first-class engineering concerns, not experiments.
• Design agent frameworks that balance autonomy with determinism, observability, and safety.
• Make pragmatic architectural trade-offs across latency, cost, accuracy, scalability, and maintainability.
• Integrate and operate LLMs (commercial and/or open-source) including model selection, fine-tuning strategies, embeddings, retrieval (RAG), and inference optimization.
• Address real-world issues: hallucinations, drift, prompt regressions, failure modes, and customer trust.
• Deploy and operate AI services across cloud platforms (AWS, Azure, GCP), including secure enterprise integrations and customer-specific deployments.
• Design scalable inference and orchestration architectures using containers, APIs, and distributed systems.
• Ensure the platform is shippable, debuggable, and supportable — not fragile or research-grade.
• Act with founder-level ownership: identify gaps, propose solutions, and move forward without waiting for perfect requirements.

To ensure you’re set up for success, you will bring the following skillset & experience:

• 10+ years of professional software development experience, with significant time shipping B2B products used by external customers.
• Strong software engineering foundation with expert-level Python and experience designing production systems.
• Proven experience building, deploying, and operating AI-powered products in production — not just prototypes or research.
  • Hands-on experience with LLMs and GenAI systems in real applications (e.g., agents, copilots, automation, decision systems).