At a Glance
- Tasks: Lead the design of AI solutions for insurance sales using cutting-edge GenAI technologies.
- Company: Join a forward-thinking tech company revolutionizing the insurance industry.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborative environment with mentorship opportunities and a focus on continuous improvement.
- Why this job: Make a real impact by developing innovative AI solutions that transform how insurance is sold.
- Qualifications: 6+ years in machine learning and strong expertise in GenAI and LLM applications.
The predicted salary is between 123750 - 151250 $ per year.
The Senior Gen AI 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 Gen AI 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 Gen AI microservices within a secure enterprise cloud environment.
The position requires strong expertise in machine learning, NLP, Gen AI 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 Gen AI 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 Gen AI 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 Gen AI 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 Open AI, Azure Open AI, Anthropic, Llama, or similar models
- Experience designing and deploying Gen AI 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 No SQL 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 Gen AIOps practices for managing AI lifecycle
- #J-18808-Ljbffr
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