Senior Engineering Manager, Data & AI
Leads Alvys's Data & AI engineering group (~8 engineers) building the Foundry, Insights, Intel, and Voice agent/analytics products for its freight TMS platform. Stays ~1/3 hands-on on LLM agents, evals, and the context graph, on .NET, TypeScript, and Python across Azure, Snowflake, dbt, Cosmos DB, and Postgres.
- Compensation
- Not disclosed
- Location
- United States
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Contract
- Experience
- 3+ years
- Category
- Software engineering
- Posted
Skills
Must have
- 8+ years building software, including 3+ years managing engineers
- Shipped production LLM systems to real users, not demos
- Daily agentic engineering with coding agents; multi-agent workflows, contracts, hooks, and loops in production
- Depth in agent systems: tool use, function calling, MCP, orchestration, memory, retrieval
- Built eval systems (offline suites, online metrics, human review, transcript analysis) used to gate releases
- Data engineering judgment: event streams, CDC, cloud warehouses, dbt, data quality
- Designed and operated production distributed systems with real reliability requirements
- Secured multi-tenant SaaS and isolated customer data; treats agent permissions as a security boundary
- Writes Python or TypeScript fluently; comfortable reading C#, F#, and .NET
- Operated at Series B/C velocity amid ambiguity with a bias toward shipping
- Clear technical communication across engineers, customers, and executives
Nice to have
- Knowledge graphs, context graphs, entity resolution, or GraphRAG in production
- Real-time voice agents: telephony, speech-to-text, text-to-speech, turn-taking, latency budgets
- Fine-tuning, RLHF, or prompt optimization against eval sets
- Embedded automation or workflow platforms such as Activepieces, n8n, or Temporal
- SRE experience or a record of running systems other teams depend on
- Logistics or freight experience, or another operations-heavy vertical
- Founder or early-stage engineering leadership experience
- History of hiring, growing, and retaining excellent engineers
Full description
Senior Engineering Manager, Data & AI
Lead the engineering team behind Foundry, Insights, Intel, and Voice: the agents, analytics, and data intelligence of the modern freight platform.
About the Role
Remote, United States. Some travel is required for team onsites, customer visits, and industry events. Relocation assistance is not provided.
The Senior Engineering Manager, Data & AI leads the engineering group behind Foundry, Insights, Intel, and Voice. You'll own the group's technical strategy, delivery, and quality, hire and develop its engineers, and stay hands-on by prototyping, writing evals, reading traces, and reviewing architecture in depth.
The team is about eight engineers today and growing. It also owns the shared foundation those products run on: a context graph that connects loads, trips, carriers, drivers, customers, documents, and conversations; event-sourced platform data streaming into the warehouse; and the evals, tracing, guardrails, and model routing every agent depends on.
Alvys facilitates more than $1 billion in funds every month across invoices, carrier payments, and driver settlements. Behind that volume is one of the richest operational datasets in freight. This team turns it into agents that do real work and intelligence customers can't get anywhere else.
Engineering leaders at Alvys are builders. Expect to spend about a third of your time building. You'll set the standard for how an AI-first team works: coding agents in the daily workflow, evals before scale, and production evidence over demos.
The platform runs on .NET (C#, and increasingly F#), TypeScript, and Python on Azure and AKS, with Cosmos DB, Postgres, Elasticsearch, Snowflake, dbt, and Fivetran underneath.
We're looking for someone who treats AI as a first-class material, both in what we build for customers and in how the engineering team works.
Industry Insight
Freight runs on phone calls, emails, PDFs, and tribal knowledge. The U.S. trucking industry is worth approximately $1 trillion today and is projected to grow by roughly 60% to nearly $1.5 trillion over the next decade.
Yet the work that moves that freight is still largely manual. Nearly 580,000 active U.S. motor carriers operate in a highly fragmented market; 91.5% run 10 or fewer trucks, and 99.3% run 100 or fewer. Few of these businesses will ever build a data team of their own. Every check call answered, rate confirmation read, and exception caught by software is time back for a small team running on thin margins.
Freight data is also structurally messy. A single load spans brokers, carriers, drivers, shippers, and documents, each with its own identifiers, and the truth lives across EDI messages, phone calls, and free text. Generic AI tooling doesn't model any of this. That's why we're building a context graph grounded in how freight actually moves.