Senior Genai Engineer

The Senior GenAI Engineer will lead the design and development of large language model (LLM) based solutions to support an AI-powered recommendation and assistant platform for insurance sales advisors. This role focuses on architecting scalable GenAI pipelines from scratch, including Retrieval-Augmented Generation (RAG), agentic workflows, and LLM integrations. The engineer will work closely with data, infrastructure, and engineering teams to develop production-ready AI solutions, evaluate models, and implement robust GenAI microservices within a secure enterprise cloud environment. The position requires strong expertise in machine learning, NLP, GenAI technologies, and cloud-native architectures, with the ability to translate business needs into scalable AI solutions.
KEY RESPONSIBILITIES
Architect and design LLM-based solutions including RAG pipelines, embeddings, fine-tuning strategies, and evaluation frameworks
Build scalable GenAI microservices and integrate them with internal enterprise systems
Develop advanced prompt engineering strategies, agentic workflows, and safety guardrails for LLM systems
Evaluate open-source and commercial LLM models for performance, cost efficiency, and risk mitigation
Collaborate with data teams to prepare training datasets, knowledge bases, and analytics pipelines
Oversee ingestion and refresh of knowledge bases and data pipelines supporting RAG architectures
Implement monitoring, validation frameworks, and closed-loop measurement to continuously improve AI solution quality
Ensure compliance with enterprise security standards and insurance regulatory requirements
Work with engineering teams to productionize GenAI solutions using scalable cloud architectures
Partner with business stakeholders to define use cases, requirements, and solution delivery strategies
Document architecture, data sources, and technical processes supporting AI solutions
Present insights, model performance metrics, and business impact to senior stakeholders
Support change management and adoption strategies for AI-driven solutions
Mentor junior engineers and promote best practices in AI engineering and system design
Collaborate with cross-functional teams including Data Infrastructure, Backend, and Frontend teams
Maintain accurate task tracking and documentation using tools such as Jira
REQUIRED QUALIFICATIONS
6+ years of experience in machine learning, artificial intelligence, or NLP engineering
Minimum 2+ years of hands-on experience working with GenAI and LLM-based applications
Strong expertise in Retrieval-Augmented Generation (RAG), vector databases, embeddings, and model evaluation techniques
Hands-on experience with LLM platforms such as OpenAI, Azure OpenAI, Anthropic, Llama, or similar models
Experience designing and deploying GenAI solutions in production environments
Proficiency in Python and experience building data pipelines for AI systems
Strong knowledge of cloud-native architectures, preferably on Microsoft Azure
Experience building scalable AI systems in enterprise environments
Strong understanding of machine learning techniques and statistical modeling methods
Experience working with SQL, data modeling, and relational or NoSQL databases
Strong problem-solving, analytical thinking, and communication skills
Ability to collaborate effectively with cross-functional teams and business stakeholders
Bachelor’s degree in Computer Science, Engineering, Mathematics, or equivalent practical experience
PREFERRED QUALIFICATIONS
Experience designing agentic AI systems and integrating AI agents into enterprise workflows
Familiarity with BI and visualization tools such as Power BI or Tableau
Experience working within financial services or insurance domain environments
Experience with Hadoop or other distributed data platforms
Strong understanding of MLOps or GenAIOps practices for managing AI lifecycle
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