Co-Founder, Senior Contextual AI and Machine Learning Engineer (Personalization, Agentic Systems, Reinforcement Learning, Python Expert) **Equity-based role** OPT OK

SQOR.ai is an AI-native Decision Intelligence platform that transforms business data into actionable insights. They are seeking a Senior Contextual AI & Machine Learning Engineer to design and scale contextualization, personalization, and reinforcement learning systems to enhance their analytical engine and improve AI performance.

Responsibilities:

• Elevate Contextual Intelligence & Personalization• Design and implement contextual modeling frameworks that improve relevance, personalization, and coherence of AI outputs
• Develop user-level and organization-level adaptation systems that align insights with goals, performance patterns, and operational signals
• Build structured context capture and retrieval mechanisms
• Partner closely with the Chief Data & AI Officer on continuous improvement of measurable response quality
• Advance Machine Learning for Analytical Systems• Design and optimize machine learning algorithms for scoring, forecasting, anomaly detection, causal inference, and trend analysis
• Develop reinforcement learning and adaptive optimization strategies that improve system performance over time
• Integrate structured analytical outputs with AI-driven reasoning layers
• Contribute to the evolution of quantitative modeling frameworks within the platform
• Develop Agentic AI & Reasoning Architectures• Architect and improve agentic AI systems that combine structured analytics, contextual memory, and reasoning workflows
• Design feedback mechanisms that enhance recommendation quality and contextual alignment
• Improve system capability to respond effectively to open-ended, decision-oriented queries
• Collaborate cross-functionally to ensure agent behavior aligns with business objectives and measurable outcomes
• Optimize Vectorization & Contextual Retrieval• Design embedding and vectorization strategies to improve semantic retrieval and contextual alignment
• Advance hybrid retrieval pipelines that combine structured data and contextual inference
• Improve memory efficiency and retrieval precision across analytical workloads
• Work with vector databases and matching engines in production environments
• System Performance & AI Infrastructure• Optimize inference quality, latency, and contextual coherence at scale
• Deploy and tune AI workloads within Google Cloud and Vertex AI environments
• Collaborate with infrastructure engineers to support scalable ML pipelines and distributed inference
• Ensure AI systems continuously evolve alongside expanding data coverage and use cases

Requirements:

• 5+ years designing and optimizing Artificial Intelligence and machine learning systems in production environments
• Deep expertise in contextual modeling, personalization systems, and reinforcement learning
• Strong grounding in statistical modeling, causal inference, and quantitative analytics
• Advanced proficiency in Python and modern AI frameworks
• Experience building agentic systems, retrieval-augmented pipelines, or contextual reasoning architectures
• Experience with vector databases and embedding systems
• Production experience within Google Cloud; Vertex AI experience strongly preferred
• Strong systems thinking across ML, NLP, and distributed architectures
• Experience improving measurable decision quality in AI systems
• Background in applied AI research or advanced contextual modeling
• Experience with reinforcement learning optimization techniques
• Familiarity with Decision Intelligence, analytics automation, or enterprise AI systems
• Experience building AI systems that generate structured, decision-oriented outputs