About Us Our partner is revolutionizing legal technology with AI-driven tools that enhance efficiency and accessibility for legal professionals. They're undertaking high-impact technology transformation, partnering with a high-volume immigration law firm to optimize workflows and streamline processes. Role Overview As an AI Engineer, you will be at the forefront of integrating AI capabilities into our legal technology platform. You'll design and implement AI systems that enhance our platform's ability to process legal documents, automate workflows, and provide intelligent assistance to legal professionals. Working with cutting-edge language models and AI frameworks, you'll help shape the future of legal technology while ensuring reliability, scalability, and ethical AI implementation. Key Responsibilities - AI System Development: Design and implement AI systems that leverage large language models for legal document processing and analysis - Prompt Engineering: Develop and optimize prompts for effective model interactions in legal contexts - Model Fine-tuning: Fine-tune and adapt models for specialized legal tasks and workflows - Evaluation Framework: Create robust testing and evaluation frameworks to ensure AI system reliability and accuracy - Integration: Work closely with backend teams to integrate AI capabilities into our existing infrastructure - Research & Innovation: Stay current with AI advancements and identify opportunities for applying new technologies Technical Proficiency - AI/ML Experience: Strong background in machine learning, natural language processing, and large language models - Programming: Proficiency in Python and popular AI/ML frameworks (PyTorch, TensorFlow, Hugging Face) - Backend Integration: Experience with RESTful APIs, microservices, and cloud platforms (AWS, GCP, Azure) - Data Management: Expertise in handling large datasets and optimizing model performance - MLOps: Experience with ML model deployment, monitoring, and maintenance in production environments Qualifications - Experience: 4-6 years of experience in AI/ML engineering, with demonstrated success in deploying production AI systems - Technical Expertise: Strong foundation in machine learning, deep learning, and natural language processing - Problem-Solving: Ability to tackle complex AI implementation challenges and optimize system performance - Communication: Excellent ability to explain technical concepts to both technical and non-technical stakeholders Perks and Benefits - Remote-First Work Environment: Enjoy the flexibility to work from anywhere, with a remote-first setup that supports work-life balance and productivity. - Competitive Salary: We offer a competitive salary to recognize and reward your skills and contributions. - Unlimited Paid Time Off: Take the time you need with an unlimited PTO policy, empowering you to recharge and maintain balance. - Professional Development: Access resources for growth, including conference passes, online courses, and training programs to support your career advancement. - Home Office Stipend: We provide a budget for setting up your home office with high-quality equipment and tools. - Latest Tech Gear: Receive a top-of-the-line laptop and any necessary peripherals to ensure you have the best tools to do your best work. - Team Retreats and Virtual Events: Participate in team-building retreats and regular virtual gatherings to connect with colleagues and strengthen our culture. - Parental Leave: We offer paid parental leave to support you and your family during this important time.

Kunai builds full-stack technology solutions for banks, credit and payment networks, infrastructure providers, and their customers. Together, we are changing the world’s relationship with financial services. At Kunai, we help our clients modernize, capitalize on emerging trends, and evolve their business for the coming decades by remaining tech-agnostic and human-centered. Join our FinTech practice in driving acquisition integrations through AI and advanced data techniques. You will support the migration of acquired personal loan platforms, specializing in data analysis, COF (Customer On File) strategy, and leveraging AI/LLM expertise to enhance our cloud ecosystem. Key Responsibilities: - Lead AI proof-of-concept initiatives for acquisitions and personal loan platform migrations. - Act as the Data Specialist and COF Acquisitions Specialist to maintain data fidelity during transitions. - Develop and implement Data Fabric Services (DFS) for personal loan platforms. - Drive AI and large language model (LLM) initiatives, collaborating with AI experts across acquisitions. - Support cross-company application migrations with a focus on data integrity and AI-enabled automation. - Provide senior-level technical expertise and strategic input on acquisition data migration projects. Qualifications: - Expertise in Python and/or Golang - Specialized experience in acquisitions data management and integration. - Strong background in AI/LLM technologies with practical application experience. - Expertise in Data Fabric Services and personal loan platform data ecosystems. - Proven capability in coordinating cross-company application migrations. - Experience with COF and other financial data strategies. - Strong communication skills and the ability to work cross-functionally. Our success over the past 20 years is rooted in our exceptional team, which thrives in a culture of collaboration, creativity, and continuous learning. We are proud to offer our employees a range of benefits, including competitive compensation, professional development opportunities, and flexible work arrangements, all designed to help them thrive. As we continue to expand, we remain committed to cultivating an environment where people feel valued, have a voice, and are given the tools to grow—both personally and professionally—while pushing the boundaries of innovation in the fintech industry. Minimum Degree Required: - Bachelor’s Degree, in lieu of a degree, demonstrating in addition to the minimum years of experience required for the role, three years of specialized training and/or progressively responsible work experience in technology for each missing year of college is required

Company Description Job Description Role: AI Architect– Insurance (Mandatory) | Azure | API-First Microservices (.NET Program) Duration: Long Term Location: Remote/ EST Experience: 15+ years overall; 4+ years in AI/ML architecture/engineering Role Summary We are building a next-generation insurance platform, including a greenfield P&C Policy Administration System (PAS) with a microservices-based, API-first architecture on Microsoft .NET. As the AI / ML Architect, you will lead the design and delivery of AI-powered capabilities across underwriting, pricing, claims, fraud, and operations. You will define end-to-end AI architecture (data → model → MLOps → serving), ensure secure and compliant AI, and partner closely with product, actuarial, underwriting SMEs, and engineering teams to move from prototypes to production-scale AI. Insurance domain experience is mandatory for this role. Key Responsibilities 1) AI Architecture & Solution Design (End-to-End) - Define the target-state AI/ML architecture for insurance use cases: underwriting decision support, risk scoring, claims triage, fraud detection, pricing optimization, customer/agent assist, and personalization. - Select and guide model approaches: predictive ML, LLMs/GenAI, NLP (and vision models where applicable), with clear tradeoffs and success metrics. - Design API-first AI services that integrate cleanly with microservices (REST/gRPC, event-driven triggers, idempotency, versioning). - Define patterns for feature pipelines, model serving, and governance that work across multiple pods and environments. 2) Model Engineering, MLOps & Deployment (Production Focus) - Lead model development lifecycle: training, evaluation, validation, release, monitoring, and periodic refresh. - Implement MLOps pipelines: automated model testing, monitoring, drift detection, model registries, approval workflows, and rollback strategies. - Define serving patterns (batch/real-time/streaming) and optimize for accuracy, latency, reliability, and cost. 3) Insurance Domain Alignment (Business + Actuarial + Underwriting) - Partner with product owners and translate requirements into AI-enabled components and measurable outcomes. - Ensure AI outputs comply with underwriting guidelines, rating practices, claims workflows, and internal governance. - Design human-in-the-loop controls where needed for regulated decisioning and operational safety. 4) Responsible AI, Security, Compliance & Risk - Establish responsible AI guardrails: explainability, fairness/bias mitigation, audit trails, traceability, and model documentation standards. - Ensure data privacy/security controls across the pipeline: PII handling, access controls, encryption, secrets management, and environment separation. - Collaborate with risk/compliance to meet insurance regulatory expectations for AI systems (governance, reproducibility, reviewability). 5) Platform Integration & Cross-Functional Leadership - Work closely with the Chief Architect, .NET architects, data architect, DevOps, and engineering pods to align AI services to platform standards. - Mentor data scientists/ML engineers; enforce engineering rigor (testing, reliability, monitoring, secure coding). - Drive POCs and technology evaluations, and productize successful capabilities into reusable platform services. 6) AI-Assisted Engineering Enablement (Claude Code, Cursor, MCP) - Use Claude Code and Cursor as first-class development accelerators (code generation, refactoring, test generation, documentation), with strong review and security guardrails. - Standardize patterns for tool usage across teams, including MCP-based workflows/integrations (where applicable), ensuring traceability and quality gates. - Define measurement for productivity and quality improvements (cycle time, rework, defect leakage, release stability). Must-Have Qualifications Insurance Domain (Mandatory) - Proven insurance industry experience is required (P&C preferred): underwriting, rating/pricing, claims triage, fraud, policy servicing, or insurance data/analytics. - Experience designing or integrating ML/AI solutions in insurance decisioning contexts (e.g., risk scoring, pricing, fraud, claims). Technical (Azure-first) - 4+ years hands-on AI/ML engineering and/or architecture experience; overall experience typically 12+ years. - Strong experience with Azure AI ecosystem, including one or more of: - Azure Machine Learning (training, registries, endpoints) - Azure OpenAI / LLM integration patterns - Azure AI Services (language, vision, etc.) - Strong MLOps experience: CI/CD for ML, model registries, monitoring, drift detection, evaluation, and controlled rollouts. - Experience building API-first services and deploying ML systems using Docker and Kubernetes (AKS preferred). Engineering & Collaboration - Strong communication skills: can explain model tradeoffs and risks to non-technical stakeholders and client executives. - Proven ability to lead cross-functional teams in fast-paced environments and ship production outcomes. - Strong P&C insurance experience (Auto/Home/Commercial) and familiarity with PAS workflows. - Experience with event streaming (Kafka/Event Hubs) and real-time inference/feature pipelines. - Experience with responsible AI frameworks and interpretable ML methods in regulated environments. - Azure certifications (Azure AI Engineer / Azure Solutions Architect). Additional Information All your information will be kept confidential according to EEO guidelines.

• Designing, developing, and deploying machine learning models and AI applications that solve complex business problems • Design, build, and maintain AI‑powered search, summarization, and chat applications • Hands‑on experience building or integrating agentic AI systems • Implement information‑retrieval solutions • Develop production‑ready AI applications in Python • Build and tune OpenSearch / Elasticsearch indexes • Evaluate, tune, and refine AI models and system performance through experimentation • Partner with product and business stakeholders to translate requirements into scalable technical solutions