AI Solution Engineer

AmeriLife

AI Solution Engineer building Databricks and Azure AI agents for AmeriLife’s insurance and financial-services operations. Delivering production ML solutions, evaluations, governance, and reusable architecture.

Posted 8/24/2026full-timeRemote • Alabama, Arizona, California, Colorado, Connecticut, Florida, Hawaii, Idaho, Illinois, Iowa, Kansas, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Minnesota, Mississippi, Missouri, Montana, Nevada, New Hampshire, New Jersey, New Mexico, New York, North Carolina, Ohio, Oklahoma, Oregon, Pennsylvania, Rhode Island, South Carolina, South Dakota, Tennessee, Texas, Utah, Vermont, Virginia, Washington, West Virginia, Wisconsin • 🇺🇸 United StatesMid-LevelSenior💰 $150,000 - $170,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying LLM-powered AI agents and systems, with a strong foundation in statistical modeling, machine learning, and cloud-native architecture. Proficient in building production-grade AI solutions, ensuring compliance, and effectively communicating technical results to stakeholders.

Highest-signal resume keywords

LLM-Based Systems DevelopmentDatabricks ExperiencePython Engineering PracticeAPI Design and IntegrationStatistical Modeling and Machine Learning

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

LLM-Based SystemsStatistical ModelingMachine LearningAdvanced SQLPySparkTime-Series ForecastingDockerCI/CDData GovernanceProduction Ownership

Soft Skills

Clear CommunicationCollaboration with Non-Technical Leaders

Tools & Technologies

DatabricksMicrosoft AzureAzure OpenAIAzure AI FoundryUnity CatalogDelta LakeMLflowGitAgent FrameworksInternal APIs

Industry Keywords

AI AgentsMulti-Step WorkflowsData GovernanceRegulated EnvironmentFairness and Unfair-Discrimination Risks

Tech Stack

Tools & technologies

AzureCloudDockerPySparkPythonSQLUnity

About the role

Key responsibilities & impact

Design, build, evaluate, and ship multi-step AI agents and LLM-powered services into production
Build AI agents that retrieve governed data, call internal APIs and tools, make bounded decisions, and escalate to humans
Engineer tool/function definitions, retrieval and grounding, state and memory, orchestration, retries, failure handling, cost, and latency management
Build golden datasets, offline and online evaluations, regression suites, human-in-the-loop review, and guardrails
Instrument and operate production systems with tracing, monitoring, drift and quality alerting, and clear ownership
Create reusable components for the shared services catalog
Partner with vertical leaders to identify and shape high-value use cases
Translate business problems into solution designs and establish reference architectures and preferred patterns
Advise on build-versus-buy and whether an agent, model, rule, or fixed process is appropriate
Build and validate forecasting, propensity, segmentation, and anomaly-detection models
Engineer Lakehouse features and pipelines serving models and agents
Design baselines, holdouts, A/B tests, quasi-experimental measurements, and defensible outcome evaluations
Communicate technical results to engineers and distribution executives
Document intended use, limitations, training-data assumptions, testing, and monitoring plans
Maintain the model inventory and apply de-identification and least-privilege access
Flag fairness and unfair-discrimination risks and route issues for actuarial and compliance review
Build auditable systems with reproducible code, lineage, methodology, and compliant recordkeeping

Requirements

What you’ll need

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
At least 2 years hands-on with LLM-based systems
Must be authorized to work in the United States without sponsorship
3+ years building AI or ML systems in production, including designing and shipping LLM-powered agents or multi-step AI workflows
Practical fluency with at least one agent framework or SDK
Experience with tool and function calling, internal APIs, structured outputs, RAG, vector search, prompt and context engineering, systematic AI evaluation, and guardrails
Strong hands-on Databricks experience with notebooks, clusters, jobs, Workflows, and production-grade code
Advanced SQL and solid PySpark
Experience with Unity Catalog, Delta Lake, medallion architecture, and MLflow
Production experience on Microsoft Azure, including Azure OpenAI or Azure AI Foundry
Strong Python engineering practice with Git-based version control
API design and integration, REST, authentication, secrets handling, and enterprise systems integration
Docker and CI/CD for data and AI workloads
Understanding of cloud-native architecture, identity and RBAC, and data governance in a regulated environment
Foundation in statistical modeling and machine learning
Experience building and validating supervised models on structured data and taking at least one to production
Time-series forecasting experience, hypothesis testing, and rigorous model evaluation
Ability to work with missing values, class imbalance, drift, and inconsistent source systems
Ability to work directly with non-technical business leaders
Full production ownership from problem definition through deployment, adoption, and iteration
Experience leading delivery at the project or pod level
Clear written and verbal communication
Background screening required

Benefits

Comp & perks

PTO
Medical insurance
Dental insurance
Vision insurance
Retirement savings
Disability insurance
Life insurance
Periodic travel to AmeriLife business locations and affiliate sites