• Design and deliver production AI and agentic systems across document intelligence, workflow automation, and copilots • Own architecture decisions for LLM-based systems, including retrieval, orchestration, memory, tool use, and evaluation • Build and maintain evals and observability frameworks to ensure system quality, reliability, and performance • Optimize systems for cost and latency at production scale • Partner closely with AI Product to scope, sequence, and deliver high-impact features • Collaborate with Data Engineering on pipelines, schemas, and data quality foundations • Mentor engineers working on AI-adjacent systems and elevate team capabilities • Evaluate vendors, models, and tools through POCs, benchmarking, and cost-performance analysis • Ship quickly, iterate in production, and continuously improve system performance

• Design, build, and deploy production GenAI systems, including LLM applications, agentic workflows, RAG pipelines, and AI-powered search capabilities. • Architect scalable AI services using modern ML frameworks, model-serving tools, APIs, Docker, Kubernetes, and CI/CD pipelines. • Develop and optimize retrieval systems using embeddings, vector databases, semantic search, reranking, and structured data sources. • Fine-tune, adapt, and evaluate LLMs for domain-specific use cases using prompt engineering, supervised fine-tuning, LoRA / QLoRA, or related methods. • Build automated evaluation frameworks to measure model quality, prompt performance, retrieval accuracy, reasoning reliability, latency, and cost. • Implement observability for AI systems, including tracing, logging, performance monitoring, drift detection, and output-quality review. • Translate prototypes and research concepts into reliable product features that can scale in production. • Partner with product managers, data engineers, backend engineers, analysts, and business stakeholders to define AI capabilities and technical tradeoffs. • Review architecture, provide technical guidance, mentor junior team members, and promote strong engineering practices. • Create clear technical documentation, implementation plans, runbooks, and model lifecycle documentation.

• Assist in developing AI-powered features using Python, LLM tools, ML libraries, APIs, and internal platform services. • Support prompt engineering, prompt testing, model comparison, and evaluation of AI-generated outputs. • Help build and maintain RAG workflows, including document preparation, chunking, metadata tagging, embedding generation, retrieval testing, and result review. • Prepare, clean, format, and validate datasets used for model testing, prompt evaluation, and AI experiments. • Assist with model and workflow evaluation by reviewing outputs, identifying errors, documenting patterns, and comparing performance across approaches. • Write clean, readable Python code for scripts, internal tools, prototypes, experiments, and service components. • Support debugging of AI workflows, data pipelines, API integrations, and model behavior under the guidance of senior engineers. • Participate in code reviews, design discussions, team planning, and documentation efforts. • Learn and apply production engineering practices, including Git workflows, testing, logging, Docker, CI/CD, and deployment basics. • Document experiments, implementation details, findings, and recommendations clearly for technical team members.

• Build production software with code and Supernal's proprietary platform, including backend services, data models, and CRUD applications • Build and maintain integrations with external systems (APIs, webhooks, third-party tools, and data sources) that AI Employees can safely act on • Design, implement, and deploy conversational agents, including multi-turn flows, state management, and tool usage • Own end-to-end technical delivery for high-priority customer implementations, from architecture through production launch • Translate customer requirements and SOWs into clear technical designs, execution plans, and deliverables • Make and own architectural decisions across application design, API integrations, LLM orchestration, RAG design, and workflow decomposition • Handle real-world voice system challenges including latency, interruptions, fallbacks, error handling, and failure recovery • Write automated tests — unit tests for isolated logic and end-to-end tests for full system and user journey validation • Apply solid error handling: distinguish retryable vs. fatal failures, surface meaningful error messages, and avoid silent failures • Actively debug complex production issues across agent logic, prompts, integrations, and external dependencies • Partner with delivery and product leadership to manage timelines, scope, and technical tradeoffs during implementation • Review technical work for quality, scalability, and maintainability, setting a high bar for engineering excellence • Define, document, and evolve best practices for building and delivering reliable AI Employees