AI Solution Engineer

Our Company Explore how you can contribute  over 50 years, Ameri Life 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.

Ameri Life is a national leader in insurance and financial services, and we are standing up an enterprise AI capability from the ground up. The model is deliberately federated: a small, senior center owns the data platform, reusable AI services, and governance — while solution architects embedded in our Health and Wealth verticals find the highest-value work and build it alongside the business.

This is one of the first of those embedded roles, and it is a builder’s job. You will spend most of your time engineering and shipping AI agents and LLM-powered services on Databricks and Azure — automating real workflows in contracting, commissions, and distribution operations where a national platform gives the economics real scale. The rest of your time draws on classic data science: the forecasting, propensity, and evaluation work that makes those solutions trustworthy and measurable.

Agentic AI engineering & implementation:
Designing, building, evaluating, and shipping multi-step AI agents and LLM-powered services into production

Solution architecture & business partnership:
Finding and shaping high-value use cases with vertical leaders; reference architecture, reusable patterns, build‑vs‑buy input

Applied data science & ML:
Forecasting, propensity and segmentation models, evaluation design, and the feature engineering behind both agents and models

Build and ship AI agents

Design, build, and deploy multi‑step AI agents that complete real business workflows — retrieving from governed data, calling internal APIs and tools, making bounded decisions, and escalating to a human when they should.

Engineer the unglamorous parts that make agents work: tool and function definitions, retrieval and grounding strategy, state and memory, orchestration, retries and failure handling, cost and latency management.

Build evaluation into the build, not after it. Golden datasets, offline and online evals, regression suites, human‑in‑the‑loop review, and guardrails you can point at when someone asks how you know it works.

Instrument and operate what you ship. Tracing, monitoring, drift and quality alerting, and a clear owner for every production surface.

Harvest reusable components into the shared services catalog so the next solution costs less than yours did.

Architect solutions with the business

Embed with your vertical’s leaders — operations, distribution, affiliate partners — observing the actual work rather than waiting on a written spec.

Translate business problems into solution designs, including the honest version: what is automatable today, what needs process work first, and what is not worth building.

Establish reference architectures and preferred patterns for your vertical, and contribute them back to the center.

Bring judgment to build‑versus‑buy and to the question of when an agent is the right answer versus a model, a rule, or a fixed process.

Apply data science where it moves the outcome

Build and validate predictive models — forecasting, propensity, segmentation, anomaly detection — that inform planning or drive an automated decision.

Engineer features and pipelines on the Lakehouse that serve both your models and your agents.

Design the measurement. Baselines, holdouts, A/B and quasi‑experimental designs, and a defensible read on whether the thing actually worked.

Communicat…