IND Staff Software Engineer - GCC011 Were determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every prospect to achieve your goals and to help others accomplish theirs, too. Join our team as we help shape the future.

Position Summary We are seeking a highly skilled T7 AI Engineer to join our engineering team in Hyderabad, India. This role combines hands-on AI/ML engineering with deep software development expertise to build, deploy, and operate production-grade AI systems at enterprise scale. You will design and implement AI-powered solutions from LLM integrations and agentic workflows to ML pipelines and intelligent automation while driving AI adoption and engineering excellence across teams.

Level: T7 (Senior Engineer) Location: Hyderabad, India Employment Type: Full-Time Key Responsibilities AI/ML Engineering &

- Delivery Design, build, and deploy production AI systems including RAG pipelines, agentic workflows, multi-model orchestration, and intelligent automation Integrate large language model (LLM) APIs and AI/ML services into enterprise applications (GCP Vertex AI) Implement and optimize prompt engineering strategies, fine-tuning pipelines, embeddings, and vector search solutions Build and maintain AI orchestration workflows using frameworks such as LangChain, Semantic Kernel, AutoGen, CrewAI, or LlamaIndex Develop custom AI agents, tools, and autonomous workflows that solve real business problems Establish evaluation frameworks for AI systems measuring accuracy, latency, cost, hallucination rates, and business outcomes Full Stack Development &




- Integration Build end-to-end AI-powered applications spanning frontend, backend, APIs, and data layers Develop robust backend services using Python (FastAPI/Django) or Node.js to support AI workloads Implement and optimize RESTful APIs, GraphQL endpoints, and event-driven integrations for AI services Build modern frontend interfaces for AI-powered features using React, Angular, or Vue.js with TypeScript Write clean, well-tested, production-ready code with a focus on maintainability and operational excellence MLOps &
- AI Infrastructure Design and implement MLOps/LLMOps pipelines for reliable model deployment, versioning, and lifecycle management Configure and manage cloud-native AI infrastructure (AWS, GCP) including model serving, orchestration, and auto-scaling Implement observability for AI systems monitoring model drift, token costs, latency, throughput, and quality metrics Build and maintain CI/CD pipelines for AI model deployment, automated testing, and continuous evaluation Design for resilience: failover strategies, fallback models, circuit breakers, and graceful degradation AI-Augmented Development Leverage AI coding assistants (GitHub Copilot, Cursor, Claude, etc.) to dramatically accelerate development workflows Use AI tools for code generation, refactoring, test writing,



documentation, and code review Develop and maintain custom AI-powered developer tools, automations, and internal platforms Establish guardrails, security practices, and governance for responsible AI usage in engineering Technical Documentation &
- Mentorship Influence engineering culture by evangelizing AI-first development practices across teams Train and upskill team members on effective use of AI tools, LLM integration patterns, and ML best practices Contribute to internal knowledge bases, tech talks, and communities of practice Partner with product, design, and data science teams to identify and deliver AI-driven opportunities Participate in architecture reviews and design discussions, ensuring AI solutions are production-ready from day one Required Qualifications Experience: 8 years of professional software engineering experience, with 2 years focused on AI/ML solution development and delivery Education: Bachelor's degree in Computer Science, Software Engineering, AI/ML, or related field (or equivalent experience) AI/ML Expertise: Strong understanding of large language model architectures, capabilities, and limitations Proven track record building and deploying production AI systems (RAG, agents, fine-tuning, embeddings, vector search) Proficiency with AI orchestration frameworks (LangChain, Semantic Kernel, AutoGen, CrewAI, LlamaIndex) Hands-on experience with major LLM providers and platforms (OpenAI, Anthropic, Google Vertex AI, AWS Bedrock) Solid understanding of prompt engineering, evaluation methodologies, and AI safety/guardrails Programming: .

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