Hands-on AI & Data Engineering Manager
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- Work arrangement
- hybrid
- Employment
- FullTime
- Stated compensation
- Not specified
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Requirements
2 with evidence7 unclear or unstated- Remote policy
- hybrid
“Provider workplace type: hybrid”
From employer posting metadata - Minimum experience (years)
- 6
“6+ years of software engineering experience building production-level systems.”
See matched passage in original description ↓ - Employment type
- FullTime
8 fields not stated in this posting
- Employer sponsorship
- Not specified
- Geographic scope
- Not specified
- Degree requirement
- Not specified
- Security clearance
- Not mentioned
- Timezone / hours
- Not specified
- Citizenship requirement
- Not mentioned
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- Not specified
- Compensation
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Original job description
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About the role
We’re looking for a Hands-on AI & Data Engineering Manager to lead a team building intelligent, large-scale consumer experiences powered by AI. In this role, you will lead engineers developing AI-driven products, work closely with data scientists, product managers, and designers, and ensure that AI capabilities—from LLMs and agents to product data insights—are translated into reliable, scalable production systems. You will be responsible not only for delivering AI features, but also for analyzing product data and user behavior to continuously improve AI performance and product outcomes. This is a technical leadership role combining engineering management, architecture ownership, AI product development, and data-driven decision making.
Key responsibilities
Lead a team of data scientists, data engineers and product analysts
Own the delivery of AI-powered product capabilities, from research and experimentation to production and operation
Drive excellence, code quality, and best development practices
Provide technical direction and hands-on guidance for complex AI systems
Drive the integration of LLMs, AI agents, and intelligent workflows into core consumer experiences
Ensure AI solutions are safe, scalable, observable, and continuously improving
Lead initiatives around product data analysis and experimentation
Analyze user interactions with product features to improve accuracy, UX, and business impact
Partner with product teams to define metrics, dashboards, and experiments that guide product improvements
Design system architectures for AI-enabled applications at scale
Evaluate and select technologies for AI platforms and data pipelines
Guide the development of prompt engineering frameworks and centralized prompt management
Ensure robust monitoring, evaluation, and feedback loops for AI outputs
Translate product and business goals into technical roadmaps and execution plans
Drive alignment between AI capabilities and measurable product outcomes
Requirements
6+ years of software engineering experience building production-level systems.
2+ years of engineering management or technical leadership experience.
Strong experience building large-scale backend systems in Python.
Experience developing modern web applications using frameworks such as React / Next / Angular / Vue.
Experience deploying and operating LLM-based systems in production, including evaluation and iteration.
Strong understanding of data pipelines, experimentation, and product analytics.
Experience with modern cloud environments such as Google Cloud Platform or Amazon Web Services.
Passion for clean code, scalable architectures, and data-driven product development.
Experience with prompt engineering, RAG architectures, and vector databases.
Experience building AI agents or autonomous workflows
Nice to Have
Experience with frameworks such as ADK, A2A, LangChain, LangGraph, or LlamaIndex or equivalent
Experience with gRPC and protobuf-based architectures
Experience building MCP servers
Background in data engineering, experimentation platforms, or ML infrastructure
About april
april is the only embedded, year-round tax platform built to power smarter financial decisions. From filing to planning to onboarding, april’s white-labeled tools bring real-time tax intelligence into the platforms people already use, helping users understand the impact of every paycheck, equity transaction, or income shift, and stay on top of tax payments throughout the year. Built to handle even the most complex tax situations, april’s AI-powered tax engine ingests data directly from partner apps to deliver accurate outcomes in record time—making tax planning and filing more connected, contextual, and accessible than ever. With API-first infrastructure and seamless data integrations, april helps partners deliver more value, deepen loyalty, and turn taxes into a strategic edge—for their clients and their business.
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