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About the Role

AmeriLife is establishing an enterprise AI capability from the ground up, with a federated model where a central team owns the data platform and reusable AI services, while solution architects embedded in business verticals build AI agents and LLM-powered services. This role is focused on engineering and shipping AI agents and LLM-powered services on Databricks and Azure to automate workflows in contracting, commissions, and distribution operations, alongside classic data science tasks like forecasting and propensity modeling.

Responsibilities

Designing, building, evaluating, and shipping multi-step AI agents and LLM-powered services into production.
Finding and shaping high-value use cases with vertical leaders; providing reference architecture, reusable patterns, and build-vs-buy input.
Building and validating predictive models for forecasting, propensity, and segmentation, and designing evaluations.
Engineering features and pipelines on the Lakehouse to serve models and agents.
Communicating results plainly to diverse audiences.
Documenting intended use, limitations, training-data assumptions, testing approach, and monitoring plans for production models and agents.
Applying de-identification and least-privilege access when working with sensitive data.
Flagging fairness and unfair-discrimination risks for relevant models and routing for review.
Building for auditability with reproducible code, documented lineage, and recordkeeping.
Build and ship AI agents that complete real business workflows.
Engineer the necessary components for agents, including tool definitions, retrieval strategy, state management, orchestration, and error handling.
Integrate evaluation into the build process, including golden datasets, offline/online evaluations, and human-in-the-loop review.
Instrument and operate shipped solutions with monitoring, alerting, and clear ownership.
Harvest reusable components into a shared services catalog.
Embed with vertical leaders to observe work and translate business problems into solution designs.
Establish reference architectures and preferred patterns for the vertical and contribute them back to the center.
Provide judgment on build-versus-buy decisions and the appropriate use of agents versus other automation methods.
Design measurements for models, including baselines, holdouts, and A/B testing.

Requirements

3+ years building AI or ML systems in production, including hands-on experience designing and shipping LLM-powered agents or multi-step AI workflows.
Practical fluency with at least one agent framework or SDK (e.g., Claude Agent SDK, LangGraph, LangChain, Databricks Mosaic AI Agent Framework, Semantic Kernel).
Experience with tool and function calling, including defining tools, wiring agents to internal APIs and data, and handling structured outputs.
Experience with RAG and grounding, including chunking and retrieval strategy, vector search, and semantic/hybrid retrieval.
Experience with prompt and context engineering as an engineering discipline, including versioning, testing, and evaluation.
Experience with systematic AI evaluation, including building eval sets, measuring quality and regression, and implementing guardrails.
Sound judgment on traditional ML versus generative AI versus deterministic automation and their trade-offs.
Strong hands-on Databricks experience, including notebooks, clusters, jobs, Workflows, and developing production-grade code.
Advanced SQL and solid PySpark for large-scale transformation and feature engineering on a Lakehouse.
Experience with Unity Catalog for governance, lineage, and access control; Delta Lake and medallion architecture patterns.
Experience with MLflow for experiment tracking, model registry, and deployment.
Production experience on Microsoft Azure, including Azure OpenAI or Azure AI Foundry, and deploying services.
Strong Python engineering practice: modular, tested, reviewable code with Git-based version control.
Experience with API design and integration, including REST, authentication, and secrets handling.
Experience with containerization (Docker) and CI/CD for data and AI workloads.
Working understanding of cloud-native architecture, identity and RBAC, and data governance in a regulated environment.
Solid foundation in statistical modeling and machine learning, with the judgment to match the method to the business problem.
Experience building and validating supervised models on structured data and taking at least one to production.
Time-series forecasting experience, and comfort with hypothesis testing and rigorous model evaluation.
Comfort with imperfect real-world data.
Demonstrated ability to work directly with non-technical business leaders to discover opportunities, frame problems, and set expectations.
Full production ownership from problem definition through deployment, adoption, and iteration.
Experience leading delivery at the project or pod level: planning, sequencing, and accountability for an outcome.
Clear written and verbal communication, including the ability to explain technical trade-offs to executives.
Bachelor’s or Master’s in Computer Science, Data Science, Engineering, Statistics, Applied Mathematics, or a related technical field, or equivalent experience with a strong portfolio of shipped work.
6–10 years of combined software, data, or AI/ML engineering experience, with at least 2 years hands-on with LLM-based systems.
Must be authorized to work in the United States without sponsorship.

Skills

AI Agents
LLM-powered services
Databricks
Azure
Data Platform
Governance
Forecasting
Propensity Modeling
Solution Architecture
Applied Data Science
Machine Learning
Claude Agent SDK
LangGraph
LangChain
Databricks Mosaic AI Agent Framework
Semantic Kernel
Tool and function calling
RAG
Prompt Engineering
AI Evaluation
Traditional ML
Generative AI
Deterministic Automation
Databricks Platform
Unity Catalog
Delta Lake
Medallion Architecture
MLflow
Azure Cloud
Azure OpenAI
Azure AI Foundry
Python
SQL
PySpark
Git
Docker
CI/CD
API Design
REST
Statistical Modeling
Supervised Models
Time-series Forecasting
Hypothesis Testing
Model Evaluation
Microsoft Copilot
Document Intelligence
LLM Fine-tuning
Delta Live Tables
Feature Store
Lakehouse Federation
Databricks Asset Bundles
Azure Data Factory
Azure Functions
Azure API Management
Azure Key Vault
Azure Entra ID
Azure DevOps
Azure Logic Apps
Infrastructure-as-code
MLOps
LLMOps
scikit-learn
XGBoost
LightGBM
statsmodels
Prophet
Observability
Evaluation Harnesses
TypeScript
Clustering
Anomaly Detection
Causal Inference
Uplift Modeling
Bayesian Methods
Experiment Design
Optimization
Simulation

Location

U.S.-based
Remote

Work Type

Remote
Hybrid

Experience Level

6-10 years combined software, data, or AI/ML engineering experience
At least 2 years hands-on with LLM-based systems

Education Level

Bachelor’s or Master’s in Computer Science, Data Science, Engineering, Statistics, Applied Mathematics, or a related technical field
Equivalent experience with a strong portfolio of shipped work

Salary/Compensations

$150,000 to $170,000

Benefits

PTO
Medical
Dental
Vision
Retirement savings
Disability insurance
Life insurance

About the Company

For over 50 years, AmeriLife has been a leader in the development, marketing and distribution of annuity, life and health insurance solutions for those planning for and living in retirement.
Associates get satisfaction from knowing they provide agents, marketers and carrier partners the support needed to succeed in a rapidly evolving industry.

Equal Opportunity

We are an Equal Opportunity Employer and value diversity at all levels of the organization. All employment decisions are made without regard to race, color, religion, creed, sex (including pregnancy, childbirth, breastfeeding, or related medical conditions), sexual orientation, gender identity or expression, age, national origin, ancestry, disability, genetic information, marital status, veteran or military status, or any other protected characteristic under applicable federal, state, or local law. We are committed to providing an inclusive, equitable, and respectful workplace where all employees can thrive.
We are committed to full compliance with the Americans with Disabilities Act (ADA) and all applicable state and local disability laws. Reasonable accommodations are available to qualified applicants and employees with disabilities throughout the application and employment process. Requests for accommodation will be handled confidentially.
We are committed to pay transparency and equity, in accordance with applicable federal, state, and local laws. Compensation for this role will be determined based on skills, qualifications, experience, and market factors.
Employment offers are contingent upon the successful completion of a background screening, which may include employment verification, education verification, criminal history check, and other job-related inquiries, as permitted by law.