• 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.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description This role involves owning the end-to-end machine learning lifecycle for production AI systems. - Own the full ML lifecycle, including data ingestion, training, validation, deployment, monitoring, retraining, and retirement. - Transition AI/ML prototypes into scalable, production-ready systems with CI/CD pipelines, automation, and observability. - Lead system design and architecture discussions, providing guidance on ML systems, MLOps, and AI infrastructure. - Develop and maintain AI-driven applications and inference services, optimizing for performance, scalability, reliability, and cost. - Integrate LLMs, generative AI, and NLP solutions into IMO Health products, focusing on unstructured clinical data. - Implement monitoring, alerting, logging, and dashboards to ensure model quality, detect drift, and maintain operational SLAs. - Build, maintain, and optimize CI/CD pipelines, automation scripts, and Infrastructure-as-Code for production ML systems. - Apply containerization (Docker, Kubernetes) and cloud infrastructure best practices to manage production environments. - Mentor and guide engineers, enforce technical standards, and drive reduction of technical debt. - Conduct root cause analysis of production defects and implement durable fixes. - Advocate for non-functional requirements (availability, scalability, reliability, maintainability) and design systems accordingly. - Collaborate cross-functionally with Product, Data Science, Architecture, and Engineering teams to align AI solutions with business goals. Qualifications - 8+ years of professional experience in software engineering, AI/ML engineering, or related roles, building and operating production-grade systems. - Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field (or equivalent experience). - Strong foundation in computer science fundamentals (data structures, algorithms, design patterns, operating systems, networking). - Expert-level coding skills in Python or Java, with a strong emphasis on production-quality software engineering practices. - Hands-on experience owning ML systems in production, including deployment, monitoring, retraining, and optimization. - Experience designing and operating CI/CD pipelines, automation, and observability for ML systems. - Deep experience with cloud platforms (AWS or Azure), containerization, and Infrastructure-as-Code. - Experience with MLOps tools and workflows (e.g., MLflow, SageMaker, Kubeflow). - Experience integrating and deploying LLMs, generative AI, and agentic systems in production environments. - Working knowledge of NLP concepts (tokenization, embeddings, classification, sequence modeling); healthcare exposure is a plus. - Experience with Elasticsearch and vector databases for embedding-based search and retrieval. - Proven ability to translate business needs into scalable, reliable technical solutions, balancing technical debt and delivery velocity. - Strong system design skills for high-performance, distributed, and scalable systems. - Excellent communication and collaboration skills across cross-functional, distributed teams. - Self-starter who can operate autonomously and own complex systems end to end. Requirements - Experience with clinical or healthcare AI applications. - Familiarity with Hugging Face, PyTorch, TensorFlow, or other modern ML frameworks. - AWS Associate-level certification (Machine Learning Engineer or Solutions Architect). Benefits - Compensation range: $170,000 - $250,000 a year. - Comprehensive benefits package.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We are seeking a high-caliber Senior Product Manager to lead the evolution of Sayari’s AI initiatives. In this role, you will be the lead strategist and primary owner of the functional logic for the backend intelligence system that transforms our massive proprietary knowledge graph into autonomous reasoning capabilities. - Partner with your Front-end AI PM counterpart, Engineering, and Business Units to transition Sayari from an analyst-focused search tool to a "System of Work". - Have product ownership over the foundational AI stack: model orchestration, automated evaluation frameworks, prompt engineering systems, and the strategy for domain-specific models (including SLMs). - Drive the backend AI product lifecycle from data quality initiatives for LLMs to self-repairing agentic workflows. - Shape the core product requirements and reasoning logic AI product lifecycle to deliver definitive answers to decision-makers in mission-critical industries. Job Responsibilities - Core AI Platform Strategy: Define and execute the roadmap for Sayari’s AI infrastructure, owning the requirements for orchestration layers, prompt management systems, and underlying platform architecture. - Model Lifecycle & SLM Ownership: Lead the strategy for model selection, fine-tuning, and deployment, evaluating trade-offs between frontier models and domain-specific Small Language Models (SLMs). - Automated Evaluation & Trust: Establish the "ground truth" for Sayari AI by defining and building rigorous evaluation frameworks. - Intelligence Orchestration: Define how our systems reason and interact, owning product requirements for agentic workflows and Model Context Protocols (MCPs). - Collaborative AI Delivery: Partner with "Front-end" Application PMs to ensure backend capabilities translate into seamless user experiences. - Investment & Outcome Strategy: Author business cases and define business outcomes for significant technical shifts in our AI stack. - Tradecraft Codification & Expert AI: Lead efforts to translate complex analyst tradecraft into automated "Skills" and agentic reasoning. - Cultural Architect: Act as a Cultural Architect for AI, promoting a 'Quality-First' mindset across the company. Qualifications - 7+ years of Product Management experience, specifically focused on backend platforms or AI/ML engines. - 2-4+ years of experience in Product Management of AI-driven solutions & Deep Generative AI Fluency. - Prior experience leveraging risk, intelligence, supply chain, or financial investigation data. - Experience managing a "Platform-as-a-Product" model. - Comfortable discussing model architecture and infrastructure trade-offs with engineering peers. - A track record of translating vague technical possibilities into high-impact, executable product requirements. - Proven ability to make high-stakes product bets in non-deterministic environments. Desired Skills & Experience - Experience defining how complex entity-relationship data should be leveraged to automate high-order reasoning tasks. - Technical familiarity with model fine-tuning, RAG (Retrieval-Augmented Generation), or the deployment of SLMs in production. Benefits - 100% fully paid medical, vision, and dental for employees and their dependents. - Generous time off; we observe all US federal holidays, close our office for a winter break (12/24-12/31), in addition to granting 18 PTO days and 10 sick days. - Outstanding compensation package; competitive commissions for revenue roles and bonuses for non-revenue positions. - A strong commitment to diversity, equity, and inclusion. - Eligibility to participate in additional benefits such as 401k match up to 5%, 100% paid life insurance (up to $100,000 coverage), and parental leave. - A collaborative and positive culture - your team will be as smart and driven as you. - Limitless growth and learning opportunities. Pay Range $185,000 - $200,000 USD

• Design and develop AI enabled services using modern engineering practices • Build scalable services within AWS aligned to solution designs • Implement APIs, integrations, and orchestration layers to support product capabilities • Optimise performance, reliability, and cost across deployed workloads • Apply security and operational best practices • Operate as an active member of the product delivery team • Translate technical designs into maintainable, production ready code • Participate in backlog refinement, sprint planning, and delivery reviews • Partner with architects, technical leads, and DevOps to support release pipelines • Support testing, deployment, and defect resolution • Document architecture decisions, configurations, and patterns • Produce clear operational documentation to support ongoing service ownership • Contribute to internal engineering standards and reusable components • Follow engineering governance and agreed standards • Raise technical risks early and propose practical solutions • Identify opportunities to improve platform stability and scalability • Contribute to patterns, accelerators, and internal playbooks