Senior Software Engineer, Applied AI

Flock Freight•Encinitas, CA

1d•$159,000 - $178,000•Hybrid

About The Position

Flock Freight is seeking a Senior Software Engineer, Applied AI to integrate AI into the company for measurable business impact. This is a hands-on, internal forward-deployed engineering role where you will embed with teams across the business to identify high-value workflows, build AI-powered agents, copilot experiences, and integrations, and help these solutions become part of daily operations. The role extends beyond reducing time to insight, aiming to help employees make better decisions, complete work faster, and improve operational outcomes. This could involve enabling self-service data access, embedding copilots into existing tools, automating repetitive tasks, or developing new agentic experiences with significant ROI. You will own problems from discovery through production, delivery, and adoption, working closely with Platform Ops and business teams to understand workflows, prototype, iterate, support rollout, and measure value. The goal is to build trusted, reusable AI capabilities that improve business performance. A secondary focus includes contributing to Freight Factory automation based on roadmap priorities. This role offers optionality with a path into broader product engineering. The team is Infrastructure Engineering, reporting to the Senior Director of Engineering, Infrastructure & Applied AI.

Requirements

3 to 5+ years of software engineering experience, with at least 1 year delivering applied AI, ML, or intelligent automation in production.
Hands-on experience with LLMs such as OpenAI, Anthropic, Gemini, or similar, including prompt engineering, RAG, function and tool calling, evaluations, and agentic patterns.
Strong software engineering fundamentals, including system design, and a demonstrated ability to ship reliable production applications at high velocity.
Strong proficiency in Python and experience with APIs, data pipelines, integrations, and cloud-based application architectures.
Experience with Git and GitHub workflows, CI/CD, observability, and modern development practices.

Nice To Haves

Experience embedding with customers or internal business teams to discover workflows and deliver solutions in ambiguous problem spaces.
Experience driving adoption of new technical capabilities, including rollout, user enablement, feedback collection, and impact measurement.
Experience with agentic AI frameworks such as LangChain, LangGraph, CrewAI, or similar, or workflow orchestration tools.
Familiarity with enterprise AI platforms such as OpenClaw, Glean, Claude Cowork, or similar copilot-style tools.
Experience with agentic process automation or RPA platforms such as Sola.
Experience in logistics, freight, or other operations-heavy industries.

Responsibilities

Embed with business and operational teams to understand their goals, workflows, pain points, and constraints, then identify where AI can produce meaningful and measurable impact.
Translate ambiguous business problems into clear solution hypotheses, prototypes, production plans, and success measures.
Build side by side with end users and Platform Ops, incorporating feedback quickly and driving solutions from initial discovery through production adoption.
Own delivery end to end, including technical design, implementation, rollout, reliability, user feedback, and iteration.
Design and build AI agents, copilot experiences, and intelligent automations using LLMs, RAG, tool calling, agentic frameworks, and enterprise AI platforms.
Connect AI capabilities to enterprise data and systems, including Snowflake, operational databases, APIs, and internal platforms, with appropriate evaluation, observability, security, and feedback loops.
Build experiences that help employees query data, make decisions, and take action conversationally inside the tools and workflows they already use.
Contribute to Freight Factory automation as product roadmap priorities dictate.
Partner with Platform Ops and business leaders to support workflow redesign, rollout, training, and change management so new capabilities become part of day-to-day operations.
Act as a trusted technical partner to non-technical teams, helping them understand what AI can do, where it is appropriate, and how to use it effectively and responsibly.
Develop lightweight enablement materials, examples, and reusable playbooks that help teams adopt AI tools and identify additional high-value opportunities.
Create feedback channels with users and continuously improve solutions based on observed behavior, adoption barriers, and business outcomes.
Define success measures before building and instrument solutions to track adoption and business impact, including cost savings, hours saved, cycle-time reduction, quality, throughput, revenue impact, or other relevant operational outcomes.
Use impact and adoption data to decide when to iterate, scale, or stop an initiative.
Create reusable integration patterns, components, evaluations, and delivery playbooks that make future AI solutions faster and safer to deploy.
Codify lessons from embedded engagements and share them across Engineering, Platform Ops, and the broader business.
Help define the technical roadmap for applied AI within corporate systems with high autonomy and ownership.