• Contribute to the design and development of AI-powered platforms and systems, working alongside senior engineers on complex initiatives. • Own end-to-end delivery of complex projects, from problem discovery and system design through production rollout and iteration. • Build AI-driven features, intelligent automation, and agentic systems that enhance customer experiences and improve team productivity. • Embed AI capabilities into existing team workflows, products, and processes, ensuring solutions integrate naturally. • Integrate AI capabilities into customer-facing products, internal tools, and operational workflows. • Build full-stack components spanning backend services, APIs, data layers, and frontend interfaces. • Translate business and operational problems into maintainable, AI-enabled technical solutions with guidance from senior team members. • Apply systems thinking to ensure reliability, performance, and security across platforms. • Collaborate with engineers, teams, and stakeholders across the company to understand requirements and deliver impactful solutions. • Participate in production support and incident response for team-owned systems, learning operational best practices. • Engage in code reviews and knowledge sharing to continuously improve skills and contribute to team growth.

• Advanced Prompt Engineering: Designing complex, dynamic prompt templates with conditional logic and efficiently reusing information and context within prompts to maximize generation quality and reasoning. • Structured Outputs & Schemas: Implementing various response schemes (JSON mode, function calling, Zod/JSON schemas) to ensure AI outputs are predictable and ready for seamless integration into application logic. • Prompt Engineering & Evaluations: Building robust evaluation pipelines and using Langfuse to collect feedback and score the quality of responses in real time. • Tracing & Debugging: Performing deep debugging of complex LLM chains using Langfuse traces to identify bottlenecks and optimize for cost, latency, and context window usage. • AI A/B Testing: Running systematic experiments across different models via OpenRouter (e.g., comparing Claude 3.5 Sonnet vs. GPT-4o) and analyzing results based on quantitative metrics. • Data-Driven Decisions: Making deployment decisions for new prompts or models strictly based on quantitative benchmarks and trace data, rather than intuition. • Output Scoring & Analysis: Developing scoring systems to analyze the “Problem → Solution” chain and identify root causes of hallucinations or logic errors using Langfuse analytics. • Model Performance & Fine-Tuning: Regularly re-evaluating model performance as new architectures emerge and performing fine-tuning when necessary to meet specific domain requirements.

• Drive technical breakthroughs in agentic systems, applied ML infrastructure, and LLM-based applications. • Define and evolve the ML/LLM strategy and technology roadmap in alignment with product development. • Act as a principal technical authority, making high-impact architectural and modeling decisions across teams. • Develop prototypes for key technologies to validate new approaches and de-risk system design. • Own the full lifecycle from research and experimentation through production deployment, monitoring, and iteration. • Translate advances in ML into scalable, production-grade systems with measurable impact. • Design how LLMs operate within agent workflows, tool use, and multi-step reasoning and long-lived execution. • Implement and refine prompting strategies, multi-agent orchestration, memory management, and human-in-the-loop controls for safety and reliability. • Establish patterns for planning, decision-making, and tool orchestration within complex systems. • Own end-to-end quality evaluation of ML-powered systems, including defining metrics, benchmarks, and testing frameworks. • Establish evaluation systems that connect model performance to task success and system-level outcomes. • Ensure systems behave predictably, safely, and reliably in production through monitoring, regression testing, and robust failure handling. • Contribute to the design of ML systems supporting the full lifecycle, including training, fine-tuning, evaluation, deployment, and monitoring. • Drive architecture decisions across model serving, routing, orchestration, and latency and cost optimization. • Work across infrastructure layers, including cloud and containerized systems, to ensure scalable and efficient deployment. • Build and deploy enterprise-grade AI systems used by global customers in production environments. • Design systems that operate reliably in regulated and constrained settings, including on-premise, air-gapped, and secure cloud environments. • Ensure systems are auditable, explainable, and compliant with regulatory and organizational requirements. • Write technical reports and design documents summarizing R&D progress, system behavior, and key decisions. • Communicate complex ML concepts and tradeoffs clearly to both technical and non-technical stakeholders. • Drive alignment across research, engineering, and product through strong technical leadership. • Mentor junior and senior engineers and researchers, raising the bar for ML rigor and system-level thinking. • Establish and propagate best practices for ML system design, evaluation, and reliability across the organization. • Influence technical direction beyond immediate teams through high-impact, cross-functional work.

• Build AI-Powered Remediation Systems: Design and implement machine learning models that can identify, diagnose, and automatically resolve system issues detected by the observability platform. • Own the AI/ML Pipeline: Take end-to-end ownership of the AI lifecycle — from data collection and preprocessing to model training, evaluation, and deployment. • Integrate with Observability Stack: Work closely with the core platform team to integrate AI solutions into the existing observability infrastructure (e.g., logs, metrics, traces). • Experiment and Iterate: Rapidly prototype and experiment with different models and approaches (e.g., anomaly detection, root cause analysis, LLM-based insights) to find what works best. • Collaborate Cross-Functionally: Partner with product, backend, and DevOps teams to align AI capabilities with user needs and infrastructure realities. • Set the Technical Direction: As an early technical hire, contribute to foundational architecture decisions and establish best practices for AI/ML within the company. • Ensure Reliability and Scalability: Build systems that perform reliably at scale and integrate safely into production environments. • Stay Ahead of the Curve: Keep up with the latest advancements in AI/ML and observability to help shape the product roadmap.