Role Description We are hiring Forward Deployed AI Engineers (FDEs) to partner with one of the largest healthcare payers in the U.S. to drive AI-led transformation across core business workflows. This is not a traditional engineering role. You will work directly with business and technology teams to design, build, and deploy AI agents that transform how healthcare operations function, while enabling teams to become AI-native and self-sufficient. You will operate at the intersection of AI, engineering, and healthcare operations, delivering real-world impact across critical payer workflows. Key Responsibilities - AI-Driven Business Transformation - Redesign healthcare processes using AI-first principles - Identify opportunities to replace or augment manual workflows with AI agents - Translate business problems into working AI solutions - Agentic AI Design & Deployment - Design, build, and deploy AI agents for enterprise workflows - Implement multi-agent systems using LLMs (prompting, memory, tool usage) - Rapidly prototype and productionize solutions using platforms such as OpenAI, Anthropic, Google (Gemini) - Forward Deployed Execution - Work directly with client teams (business + IT) to: - Whiteboard solutions - Build prototypes in days/weeks - Deploy and iterate rapidly - Operate as a trusted advisor and builder - AI-Augmented SDLC - Leverage AI across the software lifecycle: - Code generation and refactoring - Automated testing and validation - Documentation and observability - Drive productivity improvements across engineering teams - Enablement & Adoption - Train business and operations teams to: - Use AI tools and agents effectively - Build and iterate workflows independently - Develop reusable playbooks and frameworks Domain Focus (Healthcare Payer Workflows) - Claims Processing & Adjudication - Prior Authorization - Member & Provider Contact Center - Provider Data Management - Care Management & Utilization Review - Revenue Cycle & Payment Integrity Qualifications - Technical - Strong software engineering background (Python, APIs, microservices) - Experience with LLMs, prompt engineering, and AI frameworks - Familiarity with agent frameworks (LangChain, Semantic Kernel, etc.) - AI & Data - Experience building AI/ML or LLM-powered applications - Understanding of structured and unstructured data pipelines - Knowledge of model evaluation, monitoring, and governance - Cloud - Experience with AWS - Experience with Azure - Consulting Mindset - Strong communication and stakeholder management - Ability to work directly with business teams - Experience in rapid prototyping and iterative delivery Requirements - 6–12+ years in software engineering, AI, or related roles - Prior client-facing / consulting experience preferred What Makes This Role Unique - Work at the forefront of AI-driven enterprise transformation - Build real-world AI agents replacing traditional workflows - Direct business impact through a forward-deployed model - Opportunity to scale from pilot solutions to enterprise-wide programs Success Metrics - Number of workflows transformed using AI - Reduction in manual effort and cost-to-serve - Adoption of AI tools by business teams - Speed from prototype to production

Assist in developing and deploying AI models, collaborate with teams to implement solutions, contribute to AI research, and translate business requirements into AI tasks while ensuring scalability and performance standards.

• Technical reference for enterprise AI solutions, with mastery of the full solution lifecycle. • Expertise in scalability, microservices architecture, and data engineering applied to AI. • Leadership in strategic, high-impact projects with a focus on innovation and digital transformation. • Ability to define technical guidelines, roadmaps and AI strategies aligned with business objectives. • Strong emphasis on security, data governance and algorithmic responsibility. • Influence in organizational decisions, acting as a bridge between technology and executive areas. • Active contribution to the development of an AI culture within the organization, promoting training and knowledge sharing. • Systemic vision and the ability to anticipate technological trends to position the company competitively. • Leads development and integration of AI components (such as LLMs, agents, predictive models) into corporate applications and systems. • Influences architecture, technology and solution roadmap decisions. • Conducts technical diagnostics and assessments, identifying opportunities to improve and evolve existing solutions. • Facilitates communication between business and technology teams, translating strategic needs into high-impact solutions. • Prepares presentations and supporting materials for technical meetings and stakeholder alignment. • Shares knowledge on AI frameworks, APIs, and microservices. • Leads discussions with DevOps, Engineering and Security teams, ensuring solution scalability. • Leads exploration and application of cutting-edge technologies (LLMs, autonomous agents, generative AI), promoting continuous innovation.

Role Description QuoIntelligence turns millions of raw signals into finished Cyber Threat Intelligence (CTI) that security teams across Europe act on every day. The ML team is small (2 people today), and the infrastructure is lean. You will own end-to-end production systems, from improving existing NLP pipelines, fine-tuning LLMs, building evaluation frameworks, and orchestrating AI agents in the cyber domain. What You'll Do - Improve the production ML stack. - Own model evaluation end-to-end. - Ship something the stack can't do today. - Expand agent capabilities. - Work directly with the IntelOps team. AI-First in Engineering - You build with AI-assisted tools daily, but you also know when AI-generated code introduces risk. - You evaluate new AI tools critically, adopting what works and dropping what doesn't. - Every model and pipeline has a clear definition of success before it ships. Qualifications - Production ML deployment. - NLP and LLM grounding. - Comfort with messy data. - Python: It's the team's language. - Open-source mindset. - AI fluency. Nice-to-haves - Experience with agent frameworks (LangGraph, LangChain, or similar). - Fine-tuning experience with open-source models (Qwen, LLaMA, Mistral). - Experience with inference servers and popular backends (e.g. NVIDIA Triton, vLLM, etc). - Familiarity with data orchestration tools (Kestra, Airflow, Prefect). - Cybersecurity or threat intelligence domain knowledge. What We Offer - A small team where your work hits customers. - Ownership, not just tickets. - Constraints that force creativity. - Ethical red lines. - Growth trajectory. The Process - Recruiter Screen. - AI fluency screen. - Take-home task: a real evaluation problem from the team's work. - Hiring manager interview. - CEO/CTO Interview. - Offer and Background Check. We welcome applications regardless of gender, nationality, ethnic origin, religion, disability, age, or sexual identity. Diversity is key to producing high-quality intelligence.