JR and SR AI Developer

Summary

Protective Life is a life insurer transforming its software development through empowered product teams and the use of machine learning and generative AI. The AI Developer will build and integrate AI-powered features and services using large language models, machine learning, Azure, and Databricks while ensuring accuracy, security, documentation, and responsible handling of sensitive customer data.

Responsibilities

• Build AI-powered application features and services on Azure Databricks and Azure — integrating LLMs and ML models into Voyager's products, with guidance on design from senior engineers
• Implement GenAI capabilities — retrieval-augmented generation (RAG), embeddings and vector search, prompt and system design, and tool/function calling
• Develop and consume APIs and services that expose model capabilities to product surfaces, with attention to latency, reliability, and cost
• Apply evaluation, guardrails, and human-in-the-loop review to keep AI outputs accurate, safe, and appropriate for a regulated insurer
• Work with the pod's data stack — dlt (dltHub), dbt, and Dagster — to source and prepare grounding data and features for AI capabilities
• Deploy and version the models and prompts your features use with MLflow and Databricks Model Serving, following patterns set by the AI/ML Engineering Lead
• Write clean, tested, version-controlled code and ship it through Azure DevOps (ADO) CI/CD
• Instrument AI features for monitoring — output quality, latency, cost, and user feedback — and help iterate based on evidence
• Apply secure-by-default and privacy practices for sensitive customer and policyholder data used in AI features — PII handling, access control, and data minimization in prompts and context
• Collaborate with product managers and designers to refine AI features through discovery and iteration
• Contribute to responsible-AI and governance practices — evaluation evidence, documentation, and adherence to model/AI governance expectations
• Grow your craft — seek and apply feedback in code and design reviews, and share what you learn with the pod
• Design and build AI-powered application features and services on Azure Databricks and Azure — integrating LLMs and ML models into Voyager's products
• Develop GenAI capabilities — retrieval-augmented generation (RAG), embeddings and vector search, prompt and system design, tool/function calling, and agentic workflows
• Build and consume APIs and services that expose model capabilities to product surfaces, with attention to latency, reliability, and cost
• Implement evaluation harnesses, guardrails, and human-in-the-loop review to keep AI outputs accurate, safe, and appropriate for a regulated insurer
• Integrate with the pod's data stack — dlt (dltHub), dbt, and Dagster — to source and prepare grounding data and features for AI capabilities
• Deploy and version the models and prompts your features depend on using MLflow and Databricks Model Serving, in partnership with the AI/ML Engineering Lead
• Write clean, tested, version-controlled code and ship it through Azure DevOps (ADO) CI/CD
• Instrument AI features for monitoring — output quality, latency, drift, cost, and user feedback — and iterate based on evidence
• Apply secure-by-default and privacy practices for sensitive customer and policyholder data used in AI features — PII handling, access control, and data minimization in prompts and context
• Partner with product managers and designers to shape AI features through discovery and rapid, evidence-based iteration
• Contribute to responsible-AI and governance practices — documentation, evaluation evidence, and adherence to model/AI governance expectations
• Mentor less-experienced engineers and share applied-AI patterns and reusable components across the pod

Skills

• 3–5 years of software development experience, including hands-on work building AI-powered or GenAI applications
• Solid programming skills — Python required; familiarity with JavaScript/TypeScript or a JVM language a plus — with sound software-engineering fundamentals (APIs, services, testing)
• Practical experience building GenAI/LLM features — RAG, embeddings and vector search, prompt/system design, and basic evaluation
• Experience integrating models via APIs and model-serving platforms — exposure to Azure OpenAI and Databricks Model Serving / MLflow preferred
• Familiarity with the modern data stack the pod uses — dlt (dltHub), dbt, and Dagster — on a Databricks lakehouse (Delta Lake); willingness to grow here
• Experience with CI/CD (Azure DevOps / ADO preferred) and Git-based, test-supported development practices
• Working knowledge of a cloud environment (Microsoft Azure preferred), including AI/OpenAI services basics
• SQL and comfort working with data
• Attention to evaluation, documentation, and secure, compliant handling of sensitive data
• Bachelor's degree in Computer Science, Engineering, or a related field — or equivalent practical experience
• 5–8 years of software development experience, including recent, hands-on work building AI-powered or GenAI applications
• Strong programming skills — Python required; familiarity with JavaScript/TypeScript or a JVM language a plus — with solid software-engineering fundamentals (APIs, services, testing)
• Hands-on experience building GenAI/LLM applications — RAG, embeddings and vector databases, prompt/system design, tool/function calling, and structured evaluation
• Experience integrating models via APIs and model-serving platforms — Azure OpenAI and Databricks Model Serving / MLflow preferred
• Experience working with the modern data stack the pod uses — dlt (dltHub), dbt, and Dagster — on a Databricks lakehouse (Delta Lake)
• CI/CD experience with Azure DevOps (ADO) and Git-based, test-supported development practices
• Working knowledge of Microsoft Azure — compute, storage, identity, and Azure AI/OpenAI services
• Strong SQL and comfort working directly with data
• Demonstrated attention to evaluation, documentation, and secure, compliant handling of sensitive data
• Bachelor's degree in Computer Science, Engineering, or a related field — or equivalent practical experience
• Experience in financial services or insurance products (Life, Annuities, claims, servicing, or customer experience)
• Experience with agent and orchestration frameworks (e.g., LangChain, LlamaIndex, or Semantic Kernel) and vector stores (Databricks Vector Search or Azure AI Search)
• Front-end or full-stack experience delivering AI features into user-facing products
• Familiarity with responsible-AI and evaluation tooling, and with bias/fairness and explainability considerations
• Relevant certification such as Microsoft Azure AI Engineer Associate or a Databricks GenAI/ML credential
• Experience in financial services or insurance products (Life, Annuities, claims, servicing, or customer experience)
• Experience with agent and orchestration frameworks (e.g., LangChain, LlamaIndex, or Semantic Kernel) and vector stores (Databricks Vector Search or Azure AI Search)
• Full-stack or front-end experience delivering AI features into user-facing products
• Familiarity with responsible-AI and evaluation tooling, and with bias/fairness and explainability considerations
• Familiarity with model risk and governance expectations in regulated settings
• Relevant certification such as Microsoft Azure AI Engineer Associate or a Databricks GenAI/ML credential

Qualifications

Must Haves

• 3–5 years of software development experience, including hands-on work building AI-powered or GenAI applications
• Solid programming skills — Python required; familiarity with JavaScript/TypeScript or a JVM language a plus — with sound software-engineering fundamentals (APIs, services, testing)
• Practical experience building GenAI/LLM features — RAG, embeddings and vector search, prompt/system design, and basic evaluation
• Experience integrating models via APIs and model-serving platforms — exposure to Azure OpenAI and Databricks Model Serving / MLflow preferred
• Familiarity with the modern data stack the pod uses — dlt (dltHub), dbt, and Dagster — on a Databricks lakehouse (Delta Lake); willingness to grow here
• Experience with CI/CD (Azure DevOps / ADO preferred) and Git-based, test-supported development practices
• Working knowledge of a cloud environment (Microsoft Azure preferred), including AI/OpenAI services basics
• SQL and comfort working with data
• Attention to evaluation, documentation, and secure, compliant handling of sensitive data
• Bachelor's degree in Computer Science, Engineering, or a related field — or equivalent practical experience
• 5–8 years of software development experience, including recent, hands-on work building AI-powered or GenAI applications
• Strong programming skills — Python required; familiarity with JavaScript/TypeScript or a JVM language a plus — with solid software-engineering fundamentals (APIs, services, testing)
• Hands-on experience building GenAI/LLM applications — RAG, embeddings and vector databases, prompt/system design, tool/function calling, and structured evaluation
• Experience integrating models via APIs and model-serving platforms — Azure OpenAI and Databricks Model Serving / MLflow preferred
• Experience working with the modern data stack the pod uses — dlt (dltHub), dbt, and Dagster — on a Databricks lakehouse (Delta Lake)
• CI/CD experience with Azure DevOps (ADO) and Git-based, test-supported development practices
• Working knowledge of Microsoft Azure — compute, storage, identity, and Azure AI/OpenAI services
• Strong SQL and comfort working directly with data
• Demonstrated attention to evaluation, documentation, and secure, compliant handling of sensitive data
• Bachelor's degree in Computer Science, Engineering, or a related field — or equivalent practical experience

Nice to Haves

• Experience in financial services or insurance products (Life, Annuities, claims, servicing, or customer experience)
• Experience with agent and orchestration frameworks (e.g., LangChain, LlamaIndex, or Semantic Kernel) and vector stores (Databricks Vector Search or Azure AI Search)
• Front-end or full-stack experience delivering AI features into user-facing products
• Familiarity with responsible-AI and evaluation tooling, and with bias/fairness and explainability considerations
• Relevant certification such as Microsoft Azure AI Engineer Associate or a Databricks GenAI/ML credential
• Experience in financial services or insurance products (Life, Annuities, claims, servicing, or customer experience)
• Experience with agent and orchestration frameworks (e.g., LangChain, LlamaIndex, or Semantic Kernel) and vector stores (Databricks Vector Search or Azure AI Search)
• Full-stack or front-end experience delivering AI features into user-facing products
• Familiarity with responsible-AI and evaluation tooling, and with bias/fairness and explainability considerations
• Familiarity with model risk and governance expectations in regulated settings
• Relevant certification such as Microsoft Azure AI Engineer Associate or a Databricks GenAI/ML credential

Benefits

• Comprehensive health, dental and vision insurance
• Mental health benefits
• Employee assistance program
• Paid time off
• Paid parental leave
• Short-term disability
• A cultural observance day
• Contributions to healthcare accounts
• A pension plan
• A 401(k) plan with Company matching
• ProHealth Rewards, Protective’s platform to improve wellbeing while earning cash rewards
• Remote work