Senior AI Engineer

All Seniors Care Living Centres

. Incorporate LLM-powered systems into new data products and advance existing products .

Posted 10/30/2026full-timeRemote • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in integrating LLM-powered systems into data products, with a strong focus on machine learning algorithms for forecasting and user segmentation. Proven ability to collaborate with cross-functional teams to drive AI strategy and implement production ML systems.

Highest-signal resume keywords

LLM IntegrationMachine Learning AlgorithmsPython ProgrammingProduction ML SystemsData Science Experience

ATS Keywords

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Hard Skills

Machine LearningData EngineeringLLM ExperienceModel EvaluationForecastingUser SegmentationRAG MethodologiesAgentic SystemsAPIsCloud Computing

Soft Skills

Comfortable with AmbiguityFast Experimentation

Tools & Technologies

OpenAIAnthropicBedrockDatabricksAWS

Industry Keywords

AI StrategyData ProductsApp Usage DataConsumer BehaviorStock Price Estimation

Tech Stack

Tools & technologies

AWSCloudPython

About the role

Key responsibilities & impact

Incorporate LLM-powered systems into new data products and advance existing products
Help shape Apptopia’s long-term AI strategy
Spearhead LLM-powered systems for finance-focused products
Design and implement agentic workflows combining LLM reasoning, structured data, and external tools
Experiment with multi-step reasoning systems, RAG, tool orchestration, and emerging paradigms
Design, develop, and implement machine learning algorithms for app usage trend forecasting, consumer behavior prediction, stock price movement estimation, and user segmentation
Identify opportunities to extend modeling work into AI-native systems integrating predictions, reasoning, and automation
Augment core datasets with LLM-derived features, classifications, and insights
Research, identify, and test ensemble methodologies to improve model accuracy and coverage
Contribute to internal AI platforms enabling teams to leverage LLMs at scale
Evaluate new LLM capabilities, architectures, and vendors
Define frameworks for evaluating LLM quality, reliability, and cost-performance tradeoffs
Partner with product and engineering to take ideas from 0 to production
Help define best practices for prompting, memory, tool use, and system architecture

Requirements

What you’ll need

5+ years in data science, ML, data engineering, or applied AI
Experience building production ML systems end-to-end
Hands-on experience with LLMs (OpenAI, Anthropic, Bedrock, etc.)
Experience with RAG, tool use, or agentic systems
Strong Python and experience with APIs and cloud (AWS preferred)
Databricks tooling experience preferred
Comfortable with ambiguity and fast experimentation cycles
Familiarity with click stream, app usage, and/or observational panel data preferred