Role Description We are looking for a Full Stack Engineer - AI Systems to build the product layer that turns these capabilities into usable, production-grade workflows. This includes designing how agents operate, fail, recover, and deliver consistent value to users. This position is 100% Remote. Full Stack Engineer Responsibilities: - Build end-to-end product features across frontend, backend, and AI integrations. - Design agent workflows that handle planning, tool use, failure, and recovery across multiple steps. - Integrate LLMs, memory, and external tools into systems that behave reliably under real-world conditions. - Design real-time AI interactions with streaming, partial results, and tight latency constraints. - Improve system reliability, observability, and fallback mechanisms. - Collaborate closely with ML, backend, and product teams to ship features end-to-end. - Continuously iterate based on real usage and failure modes. Full Stack Engineer Outcomes: - Own and ship AI-native product features that move beyond chat into persistent, goal-driven workflows. - Design and deploy agent workflows that reliably complete multi-step tasks across tools and sessions. - Reduce latency and improve responsiveness of AI interactions while maintaining output quality. - Build robust fallback and recovery mechanisms for LLM and tool failures in production environments. - Improve the success rate and reliability of AI-driven workflows through iteration, evaluation, and monitoring. - Establish patterns and abstractions for integrating LLMs, memory, and external tools into scalable product systems. - Contribute to a product experience where AI feels proactive, consistent, and dependable over time. Qualifications - Strong experience in Full Stack Engineering (Frontend and Backend). - Artificial Intelligence (AI) experience required. - Solid understanding of system design and API architecture. - Experience working with LLMs, RAG systems, or AI-powered applications. - Ability to handle ambiguity and make pragmatic engineering decisions. - Strong ownership; able to take features from idea to production. - Comfort working in fast-moving environments with evolving requirements. - Tech Stack: AI, Anthropic, API, Artificial Intelligence, Backend, Docker, Frontend, Kubernetes, Next.js, Node.js, NoSQL, OpenAI, Claude, Open Source, LLM, Python, Pytorch, RAG Systems, and SQL. Benefits - Medical insurance - Dental - Vision - Savings Plan Options - PTO Compensation USD 140,000 - USD 190,000 yearly

• Technically oversee the architecture to identify, describe, and enrich events in historical vehicle logs using Multimodal LLMs. • Oversee the off-board ingestion and fusion of semantic scene descriptions, ego-centric kinematics, and internal autonomy telemetry to create a holistic diagnostic context for LLM inference. • Develop structured prompting templates utilizing Contextual Prompting (CP), Chain-of-Thought (CoT), and In-Context Learning (ICL) to evaluate scenarios. • Architect the integration of foundation models into the Metrics Engine (ME), designing efficient cascade filtering and log slice parallelization strategies to scale high-volume LLM inference across simulation and on-road drive logs while managing computational latency and costs. • Define, design, and implement key metrics to evaluate autonomous vehicle performance, such as lane change capability, oscillations, and braking. • Deploy and manage a Retrieval-Augmented Generation (RAG) vector database containing codified AV Driving Policies to ground off-board LLM evaluations in specific Operational Design Domains. • Serve as a technical escalation point and collaborate with Autonomy (Planner, Prediction, Perception) and Systems teams to deliver high-signal, enriched events. • Drive the transition toward Direct Vector-LLM Fusion utilizing emerging Physical AI ecosystems and open-weights Vision-Language-Action (VLA) models to process telemetry off-board without text-translation bottlenecks.

• Design, develop, and maintain end-to-end web applications using modern frontend and backend technologies. • Build responsive, high-performance user interfaces using React JS and related JavaScript or TypeScript frameworks. • Develop scalable backend services, RESTful APIs, and microservices using Node.js, Django, FastAPI, Flask, or similar frameworks. • Own full product development lifecycle activities including requirements analysis, architecture design, implementation, testing, deployment, monitoring, and continuous improvement. • Integrate AI capabilities into enterprise applications using LLMs, RAG pipelines, chatbot frameworks, and prompt engineering techniques. • Design and implement AI-enabled workflows including document ingestion, embeddings, vector search, retrieval optimization, and response generation. • Collaborate with product managers, UX designers, AI/ML engineers, DevOps teams, and business stakeholders to deliver reliable and user-focused solutions. • Ensure application security, scalability, performance, maintainability, and reliability across frontend, backend, database, and AI components. • Deploy, manage, and optimize applications and AI services on cloud platforms such as AWS, Google Cloud Platform (GCP), and Microsoft Azure. • Write clean, modular, well-tested, and maintainable code following software engineering best practices. • Mentor junior engineers, participate in code reviews, and contribute to technical design discussions and architecture decisions.

- **Prompt Engineering:** Design, test, and optimize complex prompts for accurate financial text and data extraction. - **Data Visualization:** Command generative AI tools and programmatic code to output rich analytics charts. - **Process Automation:** Build autonomous LLM workflows and agents to replace manual reporting processes. - **Model Customization:** Fine-tune or apply RAG (Retrieval-Augmented Generation) to models using financial documentation. - **Data Processing:** Clean, manipulate, and restructure raw financial files into optimized prompt payloads.