Job Title: Full-Stack AI Engineer Position Type: Full-Time, Remote Working Hours: U.S. client business hours (with flexibility for model deployments, experimentation cycles, and sprint schedules) About the Role: Our client is seeking a Full-Stack AI Engineer to design, build, and deploy AI-powered applications. This role requires bridging software engineering with applied machine learning, ensuring that models are integrated into production systems that are scalable, reliable, and user-friendly. The Full-Stack AI Engineer combines back-end services, front-end interfaces, and machine learning pipelines to deliver practical, business-driven AI solutions. Responsibilities: AI Model Integration: - Deploy pre-trained and fine-tuned ML/LLM models (OpenAI, Hugging Face, TensorFlow, PyTorch). - Wrap models in APIs (FastAPI, Flask, Node.js) for scalable inference. - Implement vector search integrations (Pinecone, Weaviate, FAISS) for retrieval-augmented generation (RAG). Data Engineering & Pipelines: - Build ETL pipelines for ingesting, cleaning, and transforming text, image, or structured data. - Automate data labeling, preprocessing, and versioning with Airflow, Prefect, or Dagster. - Store and manage datasets in cloud warehouses (Snowflake, BigQuery, Redshift). Application Development (Full-Stack): - Build front-end UIs in React, Next.js, or Vue to surface AI-powered features (chatbots, dashboards, analytics). - Design back-end services and microservices to connect models to business logic. - Ensure responsive, intuitive, and secure interfaces for end users. Infrastructure & Deployment: - Containerize ML services with Docker and deploy to Kubernetes clusters. - Automate CI/CD pipelines for model updates and application releases. - Monitor latency, cost, and model drift with MLflow, Weights & Biases, or custom dashboards. Security & Compliance: - Ensure AI systems comply with data privacy standards (GDPR, HIPAA, SOC 2). - Implement rate limiting, access control, and secure API endpoints. Collaboration & Iteration: - Work with data scientists to productionize prototypes. - Partner with product teams to scope AI features aligned with business needs. - Document systems for reproducibility and knowledge transfer. What Makes You a Perfect Fit: - Strong coder with a foundation in both full-stack development and applied ML/AI. - Comfortable building prototypes and scaling them to production-grade systems. - Analytical problem solver who balances performance, cost, and usability. - Curious and adaptable, staying current with emerging AI/LLM tools and frameworks. Required Experience & Skills (Minimum): - 3+ years in software engineering with exposure to AI/ML. - Proficiency in Python (PyTorch, TensorFlow) and JavaScript/TypeScript (React, Node.js). - Experience deploying ML models into production systems. - Strong SQL and experience with cloud data warehouses. Ideal Experience & Skills: - Built and scaled AI-powered SaaS products. - Experience with LLM fine-tuning, embeddings, and RAG pipelines. - Knowledge of MLOps practices (Kubeflow, MLflow, Vertex AI, SageMaker). - Familiarity with microservices, serverless architectures, and cost-optimized inference. What Does a Typical Day Look Like? A Full-Stack AI Engineer’s day revolves around connecting models to real-world applications. You will: - Review and refine model APIs, testing latency and accuracy. - Write front-end code to surface AI features in user-friendly interfaces. - Maintain pipelines that clean and prepare new datasets for training or fine-tuning. - Deploy updates through CI/CD pipelines, monitoring cost and performance post-release. - Collaborate with product and data science teams to prioritize AI features that solve real user problems. - Document workflows and results so solutions are repeatable and scalable. In essence: you ensure AI moves from prototype to production — reliable, compliant, and impactful. Key Metrics for Success (KPIs): - Successful deployment of AI features to production on schedule. - Application uptime ≥ 99.9% and inference latency < 500ms for key endpoints. - Reduction in manual workflows replaced by AI features. - Model performance tracked and stable (accuracy, drift, false positives/negatives). - Positive user adoption and satisfaction of AI-driven features. Interview Process: - Initial Phone Screen - Video Interview with Pavago Recruiter - Technical Assessment (e.g., deploy a small ML model with API endpoints and basic front-end integration) - Client Interview(s) with Engineering Team - Offer & Background Verification

Job Title: Full-Stack AI Engineer Position Type: Full-Time, Remote Working Hours: U.S. client business hours (with flexibility for model deployments, experimentation cycles, and sprint schedules) About the Role: Our client is seeking a Full-Stack AI Engineer to design, build, and deploy AI-powered applications. This role requires bridging software engineering with applied machine learning, ensuring that models are integrated into production systems that are scalable, reliable, and user-friendly. The Full-Stack AI Engineer combines back-end services, front-end interfaces, and machine learning pipelines to deliver practical, business-driven AI solutions. Responsibilities: AI Model Integration: - Deploy pre-trained and fine-tuned ML/LLM models (OpenAI, Hugging Face, TensorFlow, PyTorch). - Wrap models in APIs (FastAPI, Flask, Node.js) for scalable inference. - Implement vector search integrations (Pinecone, Weaviate, FAISS) for retrieval-augmented generation (RAG). Data Engineering & Pipelines: - Build ETL pipelines for ingesting, cleaning, and transforming text, image, or structured data. - Automate data labeling, preprocessing, and versioning with Airflow, Prefect, or Dagster. - Store and manage datasets in cloud warehouses (Snowflake, BigQuery, Redshift). Application Development (Full-Stack): - Build front-end UIs in React, Next.js, or Vue to surface AI-powered features (chatbots, dashboards, analytics). - Design back-end services and microservices to connect models to business logic. - Ensure responsive, intuitive, and secure interfaces for end users. Infrastructure & Deployment: - Containerize ML services with Docker and deploy to Kubernetes clusters. - Automate CI/CD pipelines for model updates and application releases. - Monitor latency, cost, and model drift with MLflow, Weights & Biases, or custom dashboards. Security & Compliance: - Ensure AI systems comply with data privacy standards (GDPR, HIPAA, SOC 2). - Implement rate limiting, access control, and secure API endpoints. Collaboration & Iteration: - Work with data scientists to productionize prototypes. - Partner with product teams to scope AI features aligned with business needs. - Document systems for reproducibility and knowledge transfer. What Makes You a Perfect Fit: - Strong coder with a foundation in both full-stack development and applied ML/AI. - Comfortable building prototypes and scaling them to production-grade systems. - Analytical problem solver who balances performance, cost, and usability. - Curious and adaptable, staying current with emerging AI/LLM tools and frameworks. Required Experience & Skills (Minimum): - 3+ years in software engineering with exposure to AI/ML. - Proficiency in Python (PyTorch, TensorFlow) and JavaScript/TypeScript (React, Node.js). - Experience deploying ML models into production systems. - Strong SQL and experience with cloud data warehouses. Ideal Experience & Skills: - Built and scaled AI-powered SaaS products. - Experience with LLM fine-tuning, embeddings, and RAG pipelines. - Knowledge of MLOps practices (Kubeflow, MLflow, Vertex AI, SageMaker). - Familiarity with microservices, serverless architectures, and cost-optimized inference. What Does a Typical Day Look Like? A Full-Stack AI Engineer’s day revolves around connecting models to real-world applications. You will: - Review and refine model APIs, testing latency and accuracy. - Write front-end code to surface AI features in user-friendly interfaces. - Maintain pipelines that clean and prepare new datasets for training or fine-tuning. - Deploy updates through CI/CD pipelines, monitoring cost and performance post-release. - Collaborate with product and data science teams to prioritize AI features that solve real user problems. - Document workflows and results so solutions are repeatable and scalable. In essence: you ensure AI moves from prototype to production — reliable, compliant, and impactful. Key Metrics for Success (KPIs): - Successful deployment of AI features to production on schedule. - Application uptime ≥ 99.9% and inference latency < 500ms for key endpoints. - Reduction in manual workflows replaced by AI features. - Model performance tracked and stable (accuracy, drift, false positives/negatives). - Positive user adoption and satisfaction of AI-driven features. Interview Process: - Initial Phone Screen - Video Interview with Pavago Recruiter - Technical Assessment (e.g., deploy a small ML model with API endpoints and basic front-end integration) - Client Interview(s) with Engineering Team - Offer & Background Verification

Do you thrive and have demonstrated experience turning bold ideas into working AI products? Do you want to lead engineering for two new AI products with no legacy code, full ownership, and direct access to customers and the CEO? ​ Come Join the New AI Team At ReSource Pro! This role sits within a brand-new, 10-person, autonomous innovation group reporting directly to the CEO. The AI Factory brings together senior engineering, product, and design talent to build two net-new customer-facing AI products - from zero to production - for the insurance industry. You won’t be inheriting legacy code. You’ll be helping to architect and build new products on a new foundation. This is a hands-on engineering role in a startup-like environment, backed by the credibility, reach, and customer base of a 14,000-person enterprise. ReSource Pro has spent two decades becoming a trusted name in insurance operations. This new AI development center is its next chapter, translating deep domain credibility into breakthrough digital products. Why This Role Matters The AI Factory is built to prove what’s possible with AI in insurance - claims, operations, and beyond. - Build AI products from scratch Not prototypes or internal tools, but real systems customers use daily. - Operate with startup speed Autonomy, pace, and experimentation, backed by enterprise support. - Work directly with customers Understanding their workflows, debugging live pilots, and shaping the technical direction from real-world feedback. - Set the technical bar Engineering excellence, system design, code quality, observability, and performance for a founding team. As the Lead Engineer, you will: Lead Engineering from Concept to Launch - Own technical architecture, design, and implementation for new AI driven insurance products. - Translate open ended opportunities into defined system designs and deliver production grade solutions. - Make foundational decisions related to architecture, infrastructure, data flows, and scaling. Build Fast and Ship Fast - Partner closely with product, design, and data science to execute rapid build → test → learn → deploy cycles. - Develop prototypes and production features using Python, modern frameworks, LLM APIs, vector databases, and cloud native services. - Work across integrations, backend workflows, orchestration layers, and customer facing features. Hands On Engineering Ownership - Write full stack code with a focus on quality, reliability, and performance. - Establish best practices for versioning, CI/CD, monitoring, model evaluation, and customer integrations. - Lead small pods of engineers while supporting cross functional collaboration. Direct Customer Engagement - Participate in customer sessions to observe workflows and validate product direction. - Triages issues in real time and adjust technical direction based on practical use. - Co design features with early adopters to ensure strong market fit. Operate Like a Startup - Prioritize aggressively to focus on features that drive customer value or learning. - Define engineering rituals, coding standards, and team operating norms. - Partner with executive leadership to communicate milestones and influence strategy. What You Bring Experience - 7+ years of hands on engineering experience. - Expert at leading greenfield or 0→1 product builds required - Strong background working with AI powered systems, automation, workflow engines, or data intensive architectures. - Experience collaborating closely with product and design teams. - Cloud deployment experience, preferably AWS. - Startup or small team operating experience. Skills - Strong system design ability across backend, AI workflows, automation, and distributed systems. - Proficiency in at least one modern programming language with Python preferred. - Experience integrating LLMs, embeddings, prompt orchestration, and evaluation tools. - Ability to move rapidly from prototype to reliable production code. - Strong instincts for reliability, logging, observability, and performance. - Clear communicator with ability to articulate technical tradeoffs. Mindset - Builder mindset with drive to turn ideas into working systems. - Comfortable operating in ambiguity with speed. - Balances creative problem solving with engineering rigor. - Passionate about the future of AI in enterprise software. - High ownership and collaborative approach. Compensation Our salary ranges are based on paying competitively for our size and industry, and are one part of the total compensation package that also includes annual bonus eligibility, benefits, and other opportunities at ReSource Pro. Individual pay decisions are based on a number of factors, including qualifications for the role, experience level, skillset, geography, and balancing internal equity relative to other ReSource Pro employees. This is a remote position, and the salary range for most locations for this role is $106,561-$180,645 annually. The salary range may vary based on experience and on the specific geographic location in which the candidate resides. All remote positions are based in the United States, and candidates must reside within the U.S. to be eligible for consideration. About ReSource Pro: Focused exclusively on the insurance industry, ReSource Pro is the trusted partner insurance organizations rely on to optimize performance, streamline operations and process engineering, and drive growth. Serving 2,000+ carriers, brokers, wholesalers, and MGAs, ReSource Pro is a recognized market leader in insurance workflow optimization, data and technology services, and strategic operating model transformation. Maintaining a 96%+ client retention rate for over a decade, ReSource Pro is the only firm serving the insurance industry to have earned a spot on the Inc. 5000 list 16 times—placing it among the top 0.02% of repeat honorees across all sectors in the Inc. list’s 40+ year history. Equal Employment Opportunity Policy ReSource Pro provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

• Design and implement AI-enabled solutions on SAP BTP (SAP Document AI, SAP AI Foundation, Generative AI Hub, SAP BTP Integration). • Build production-ready AI use cases including intelligent document processing, RAG, and AI agents. • Integrate with OpenText Extended ECM. • Define clean-core compliant architectures using SAP BTP extensibility patterns. • Ensure enterprise-grade security, governance, and data privacy compliance.