Senior GenAI ML Engineer - Full Remote Globally

ABOUT THE OPPORTUNITY Join a fast-growing global insurtech company as a Senior GenAI ML Engineer and drive the development of next-generation generative AI solutions that transform how financial institutions deliver protection and insurance services worldwide. You'll be working for an insurtech ecosystem serving customers across 35+ markets in Asia, Europe, North America, and Africa , building and operating AI-driven products that make it easier for customers to get the right protection at the point of need. The company is moving beyond traditional software toward delivering automation, intelligent agents, and operational outcomes that significantly modernize business operations and reduce costs. This role offers the opportunity to innovate in the insurtech environment , working on LLMs, prompt engineering, system integration, and agents orchestration using frameworks like Langchain, AWS Bedrock , and similar technologies. You'll collaborate closely with the Head of AI, data scientists, engineers, and product managers to deliver GenAI-powered Contact Centers, document processing automation, claims decisioning systems , and other AI-driven solutions across languages and modalities. Critical Requirements: This is a senior position requiring 6+ years of AI/ML experience with at least 3+ years specifically in ML engineering, NLP, Generative AI, and LLM technologies . You must have hands-on experience with LLM Agentic workflows, RAG systems, prompt engineering , and modern GenAI frameworks. Voice Conversational AI experience is a significant advantage for this role. PROJECT & CONTEXT You'll be driving the global GenAI strategy and implementation , bringing expertise to leverage Large Language Models across multiple languages and modalities for AI-driven insurance and protection products. The work spans customer-facing applications like intelligent contact centers and chatbots, operational automation including document processing and claims decisioning, and internal AI agents that support business processes and decision-making. Your core technical responsibilities center on building and integrating generative AI applications for customer interactions using LLMs and orchestration frameworks like Langchain, LangGraph, and LlamaIndex. You'll design, develop, and scale internal AI agents and customer-facing agentic solutions including GenAI-powered contact centers that handle customer inquiries, provide policy information, and support insurance operations across multiple languages and time zones. Working extensively with AWS Bedrock , you'll design and deploy custom solutions leveraging foundation models from leading providers (Anthropic Claude, Amazon Titan, Cohere, etc.), selecting appropriate models for different use cases, optimizing for performance and cost, and integrating these models into production applications. You'll also handle fine-tuning and evaluating large language models using both proprietary insurance/claims data and external datasets to improve model performance for domain-specific tasks. Production engineering is critical - you'll build scalable APIs and backend services to support real-time AI inference, ensuring systems can handle high-volume customer interactions with low latency and high reliability. This includes implementing RAG (Retrieval-Augmented Generation) systems to ground LLM outputs in accurate insurance knowledge bases, policy documents, and regulatory information, ensuring responses are factually correct and compliant. Quality, safety, and governance are paramount in regulated insurance environments - you'll ensure reliability, privacy, and accuracy of GenAI responses by applying rigorous testing and monitoring tools, implement guardrails to prevent harmful or incorrect outputs, and contribute to governance efforts ensuring solutions follow responsible AI principles including transparency, data privacy, and compliance with insurance industry standards and regulations. Your work will support the global GenAI roadmap , bringing expert insights on technologies to adopt, tracking industry trends, and recommending tools or approaches to improve system performance and capability. You'll collaborate extensively with data science teams to apply generative AI to various business areas including document processing (policy documents, claims forms), claims decisioning (automated claims assessment), and reporting and analytics . MLOps and LLMOps practices are essential - you'll implement and manage distributed training pipelines for LLMs to ensure scalability and efficiency, establish versioning and deployment practices for LLM applications, monitor model performance and drift in production, and automate retraining workflows. Understanding transformer architectures, prompt engineering techniques, LLM evaluation methodologies , and the latest advances in generative AI is fundamental to the role. Working in a global, distributed team requires excellent communication - documenting solutions clearly, collaborating with engineering, product, and customer teams to align requirements and outputs, and articulating complex technical ideas to cross-disciplinary internal and external stakeholders. The role demands someone who is proactive, curious, collaborative, adaptable , and excellent at communication. Core Tech Stack: Python (primary), LLM frameworks (Langchain, LangGraph, LlamaIndex), AWS Bedrock, foundation models ML Frameworks: PyTorch, scikit-learn, Hugging Face Transformers Cloud Platform: AWS (preferred) with ML services (Bedrock, SageMaker, Lambda) Focus Areas: Agentic AI, RAG systems, prompt engineering, LLM fine-tuning, conversational AI Domain: Insurtech - insurance, protection products, claims processing, customer service Scale: Global deployment across 35+ markets, multiple languages, high-volume customer interactions WHAT WE'RE LOOKING FOR (Required) AI/ML Experience: 6+ years of experience in AI and machine learning with proven track record across different ML domains GenAI Specialization: 3+ years of specific experience in machine learning engineering, NLP, Generative AI, and LLM technologies - this is the core requirement LLM Agentic Workflows: Hands-on experience with LLM Agentic workflows and frameworks including Langchain, LangGraph, LlamaIndex , or similar orchestration tools Transformer Architectures: Knowledge and experience with transformer architectures including BERT, GPT, T5, and modern LLM architectures Prompt Engineering: Expertise in prompt engineering techniques - few-shot learning, chain-of-thought, prompt optimization, and testing RAG Systems: Experience with retrieval-augmented generation (RAG) including vector databases, embedding strategies, and retrieval optimization Guardrails Implementation: Knowledge of guardrails for LLM safety, content filtering, and output validation LLM Evaluation: Experience with LLM evaluation methodologies including benchmarking, human evaluation, automated metrics, and A/B testing MLOps/LLMOps: Hands-on experience with MLOps and LLMOps practices including model versioning, deployment, monitoring, and retraining automation Distributed Training: Knowledge of design, implementation, and management of distributed training pipelines for LLMs to ensure scalability and efficiency Python Advanced: Advanced knowledge of Python for ML engineering with strong coding practices and software engineering principles ML Frameworks: Experience with ML frameworks including PyTorch, scikit-learn , and others for training, fine-tuning, and deploying generative models AWS Cloud: Experience with Cloud technology, preferably AWS including ML services (Bedrock, SageMaker), compute, storage, and deployment API Development: Ability to build scalable APIs and backend services for real-time AI inference Production ML: Experience deploying and maintaining production-grade ML systems with proper monitoring and reliability Testing & Monitoring: Skills in applying testing and monitoring tools to ensure GenAI system reliability, privacy, and accuracy Responsible AI: Understanding of responsible AI principles including transparency, fairness, data privacy, and regulatory compliance Documentation: Strong documentation skills for technical solutions, architectural decisions, and system designs Problem-Solving: Excellent analytical and problem-solving skills for complex ML engineering challenges Collaboration: Ability to contribute both independently and as part of a team with strong collaborative mindset Communication Excellence: Excellent listening, communication, interpersonal, and presentation skills to articulate complex technical ideas to cross-disciplinary audiences English Proficiency: Proficiency in spoken and written English at minimum B2/C1 level for global team collaboration and stakeholder communication Location: Can be based anywhere globally with availability for fully remote work NICE TO HAVE (Preferred) Voice Conversational AI: Experience with Voice Conversational AI - this is a BIG PLUS for the role (speech recognition, text-to-speech, voice agents) Insurance/Fintech Domain: Previous experience in insurance, insurtech, or financial services with understanding of industry regulations and requirements AWS Bedrock Expertise: Deep hands-on experience specifically with AWS Bedrock and foundation model deployment Multiple Foundation Models: Experience working with various foundation models (Claude, GPT-4, Gemini, Cohere, Llama) and understanding their strengths Fine-Tuning Advanced: Advanced experience fine-tuning LLMs including LoRA, QLoRA, and full fine-tuning approaches Multi-Modal AI: Experience with multi-modal models handling text, images, audio, or video Multi-Lingual NLP: Experience building multi-lingual NLP systems across different languages Advanced RAG: Deep expertise in advanced RAG techniques - hybrid search, re-ranking, query expansion, contextual compression Vector Databases: Hands-on experience with vector databases (Pinecone, Weaviate, Chroma, FAISS) for semantic search LLM Agents Advanced: Deep knowledge of advanced agentic patterns - ReAct, tool use, planning, multi-agent systems Reinforcement Learning from Human Feedback: Understanding of RLHF and preference tuning for LLMs Document Processing: Experience applying AI to document understanding and processing (OCR, layout analysis, information extraction) Claims Processing: Specific experience with claims decisioning or automated underwriting systems Contact Center AI: Experience building AI-powered contact center solutions with voice and text channels Streaming Inference: Experience with streaming LLM inference for real-time conversational experiences Cost Optimization: Skills in optimizing LLM inference costs through caching, batching, model selection, and prompt optimization Security & Privacy: Deep understanding of data privacy and security in AI systems, particularly for regulated industries Kubernetes: Experience deploying ML workloads on Kubernetes for scalability Model Compression: Knowledge of model quantization, distillation , and other compression techniques Alternative Clouds: Experience with Azure OpenAI, Google Vertex AI , or other cloud ML platforms Research Background: Publications or research contributions in NLP, GenAI, or LLM domains Open Source Contributions: Active contributions to LLM or GenAI open source projects Location: Anywhere Globally (100% Remote)