JR and SR AI Developer

KEY RESPONSIBILITIES

JUNIOR:
 
• 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.
 
SENIOR:
• 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.

QUALIFICATIONS

JUNIOR:

REQUIRED QUALIFICATIONS

• 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.

PREFERRED QUALIFICATIONS

• 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.
 
SENIOR

REQUIRED QUALIFICATIONS

• 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.

PREFERRED QUALIFICATIONS

• 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.