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84.51 Degrees

Data analytics for retail marketing decisions

Lead AI/ML Engineer - P4363

AI Application Engineering +1Updated on Nov 9
Full-time

About the Job

Requirements

  • A bachelor's degree or higher in Computer Science, Computer Engineering, Software Engineering, or a related field.
  • At least 4 years of experience developing cloud-based software solutions and understanding design for scalability, performance, and reliability.
  • At least 4 years of experience applying programming languages, software design patterns, and modern engineering technologies to build production solutions.
  • At least 2 years of hands-on experience building and maintaining scalable AI-powered applications or agentic systems.
  • Hands-on experience across the end-to-end software development lifecycle, using CI/CD, automated testing, and DevOps practices to develop, deploy, and maintain scalable solutions.
  • Experience delivering production-grade software services or agentic solutions that meet stakeholder needs.
  • Strong time and project management skills, including balancing simultaneous work items and prioritizing as necessary.
  • Experience working with a managed agent platform such as LangSmith to support agent deployment, tracing, evaluation, and lifecycle management.
  • Experience preparing, validating, and organizing governed AI-ready data and metadata for reliable retrieval, context grounding, and agent use.
  • Experience working with a managed data platform such as Databricks or Snowflake.
  • Strong skills in Python.
  • Strong data engineering and data modeling skills, including integrating data sources and building reliable data pipelines.
  • Experience with Kubernetes and Docker.
  • Experience with CI/CD pipelines.
  • Experience with Terraform.

Responsibilities

  • Serve as the technical engineering lead on complex, multi-month projects to build and maintain agentic AI systems across organizational domains.
  • Foster a collaborative and innovative team environment, encouraging professional growth and development among junior team members.
  • Use established software engineering patterns, frameworks, and tools to automate, deploy, and integrate generative AI and agentic solutions.
  • Develop tools, processes, and operational capabilities to monitor agent behavior, response quality, data accuracy, latency, and system reliability.
  • Partner with researchers, domain experts, and platform teams to optimize and scale agentic systems using software engineering, DevOps, and AI lifecycle best practices.
  • Package agent capabilities, tools, and supporting services as reusable APIs and components that can be integrated across the organization.
  • Build, steward, and maintain production-grade agentic systems and supporting backend services, including agent APIs, asynchronous jobs, conversation state, file storage, and enterprise integrations, focusing on security, reliability, observability, scalability, and performance.
  • Evaluate emerging generative AI and agent frameworks, orchestration patterns, protocols, and tooling to identify opportunities for enterprise adoption and evolve existing systems.
  • Translate business requirements into consumable agent capabilities and services, balancing scale, risk, response quality, latency, and cost to maximize value.
  • Reduce time to delivery through automated build, test, and deployment pipelines, coordinated service releases, continuous evaluation, user feedback, and monitoring practices.
  • Be responsible for code reviews and quality assurance sign-off.
  • Maintain technical documentation, version control, deployment and rollback guidance, and knowledge-sharing practices to support collaboration and operational continuity.
  • Make time-sensitive decisions and solve urgent problems without escalation.

Desired Qualifications

  • At least 2 years of experience with generative AI technologies.
  • At least 2 years of experience in tech consulting, retail, or related professional services.
  • Knowledge of generative AI application patterns, including retrieval-augmented generation, tool use, and agent orchestration.
  • Experience writing agent evaluations.
  • Experience with prompt tuning.
  • Experience working with Model Context Protocol servers.
  • Experience with GitHub Actions.

About the Company

84.51 Degrees

Data analytics for retail marketing decisions

84.51 Degrees analyzes large volumes of consumer data to help retailers and consumer goods companies understand shopper behavior and tailor marketing strategies. Its main product is data analytics and predictive analytics tools that uncover consumer insights, trends, and preferences across generations. These insights are delivered to B2B clients via subscriptions, project-based work, or performance-based pricing, enabling clients to make data-driven decisions and optimize customer engagement across the purchasing journey. The company differentiates itself through a strong emphasis on culture and inclusive workplace values, which it views as important for driving collaboration and innovation. Overall, the goal is to help clients stay relevant and competitive by translating data into actionable marketing and merchandising strategies.

Company size
1,001-5,000
Company stage
M&A
Total funding
$5.5M
Headquarters
Cincinnati, OH
Founded
2015

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