Senior Engineering Manager, Data & AI

Alvys

6h•Remote

About The Position

Alvys is seeking a Senior Engineering Manager to lead the engineering group responsible for Foundry, Insights, Intel, and Voice, along with the shared data and AI foundation behind those products. The portfolio includes an embedded agent and automation platform, conversational analytics, data intelligence, AI voice and chat agents, a freight context graph, event-streaming data infrastructure, and the evaluations, tracing, guardrails, and model routing used by agents. The team has about eight engineers today and is growing. The manager will own the group’s technical strategy, delivery, and quality; hire, coach, and develop engineers; and remain hands-on, spending about a third of their time prototyping, writing evaluations, investigating traces, debugging production, and reviewing designs and code. The role includes setting standards for an AI-first engineering team, making architecture, model, vendor, and investment decisions across four products, and building agents that improve based on outcomes and user feedback. The work emphasizes reliable, measurable, and secure production AI. Responsibilities include making evaluations a release gate; maintaining a trustworthy context graph and data platform; protecting tenant data through least privilege and isolation; and ensuring that agent actions have appropriate permissions, human approvals, and audit trails. The manager will partner with product and design leaders, customers, and Forward Deployed Engineering, and report to the Head of Engineering. The platform uses .NET (C# and increasingly F#), TypeScript, and Python on Azure and AKS, with Cosmos DB, Postgres, Elasticsearch, Snowflake, dbt, and Fivetran. The position is remote within the United States, with some travel for team onsites, customer visits, and industry events. Relocation assistance is not provided.

Requirements

At least 8 years of software-building experience, including at least 3 years managing engineers.
Experience growing senior engineers and tech leads while remaining a hands-on builder able to prototype, debug production, and credibly review code.
Experience shipping LLM-powered products to real users and explaining failures, measurements, and resulting changes.
Daily use of coding agents in engineering work, with examples of multi-agent workflows, contracts, hooks, loops, and verification checks used in production.
Experience building agent systems using tool use, function calling, MCP, orchestration, memory, and retrieval, with an understanding of agent failure modes.
Experience building evaluation systems that combine offline suites, online metrics, human review, and transcript analysis, and using them to gate releases.
Experience with event streams, CDC, cloud warehouses, dbt, and data quality, with sound judgment about how data shapes constrain model capabilities.
Experience designing and operating production distributed systems with real reliability requirements.
Experience securing multi-tenant SaaS and isolating customer data, with agent permissions treated as a security boundary.
Fluency in Python or TypeScript and comfort reading C#, F#, and .NET.
Experience operating at Series B/C velocity amid ambiguity, limited support, competing priorities, and a bias toward shipping and learning.
Ability to explain technical tradeoffs to engineers, customers, and executives.
Ability to match AI autonomy to task risk and verify results using evidence such as diffs, acceptance checks, evaluations, and production monitoring.
Understanding of models, context, tools, harnesses, and memory, including their limitations.

Nice To Haves

Production experience with knowledge graphs, context graphs, entity resolution, or GraphRAG.
Experience with real-time voice agents, including telephony, speech-to-text, text-to-speech, turn-taking, and latency budgets.
Experience with fine-tuning, reinforcement learning from feedback, or prompt optimization against evaluation sets.
Experience with embedded automation or workflow platforms such as Activepieces, n8n, or Temporal.
SRE experience or a record of operating systems on which other teams depend.
Logistics or freight experience, or experience in another operationally data-entangled industry.
Founder or early-stage engineering leadership experience.
A track record of hiring, developing, and retaining excellent engineers.
Experience with F# or another functional language.

Responsibilities

Manage, coach, and develop a team of about eight engineers, including senior engineers and tech leads, and grow the team as the portfolio expands.
Hire senior and staff engineers and maintain a high quality bar as the team scales.
Establish an AI-first engineering workflow and standards for reviewing, testing, and shipping AI-written code.
Set technical direction for agents, data, and the context graph, and make build, buy, and model decisions with cost, quality, and speed in view.
Set priorities and make tradeoffs across four products sharing one platform.
Own the agent development lifecycle from prototype through production and continuous improvement.
Build measurable self-learning loops using outcomes, user corrections, and transcripts.
Make evaluations for quality, safety, latency, and cost a release gate for agents.
Own the context graph linking loads, trips, carriers, drivers, customers, documents, and conversations, including entity resolution.
Partner on the data platform, including event streams, CDC into Snowflake, data quality, and lineage.
Capture decision traces so precedent is searchable while rules remain explicit.
Combine third-party, licensed, and proprietary data sources into actionable intelligence while respecting provenance, licensing terms, and tenant boundaries.
Design agents that change data with scoped permissions, human approvals, audit trails, and defenses against prompt injection and tool misuse.
Enforce tenant isolation and least privilege across agents, tools, and data paths.
Own reliability and unit economics; track cost per task, call, and tenant and maintain healthy margins as usage grows.
Spend about a third of the time building, prototyping, writing evaluations, reading traces, and debugging production.
Review designs and code in depth and assess models and tools based on production performance rather than demonstrations.
Partner with product and design leadership on direction, architecture, sequencing, and delivery.
Work directly with customers and Forward Deployed Engineering and turn recurring patterns into platform features.
Help engineers build goal-based agent loops with upfront specifications and checks that are not weakened to pass.
Develop systems that steer agents through context, harnesses, and workflows, with few handoffs and explicit agent contracts.
Build toward continuously running agents in hosted sandboxes with least-privilege identities, named owners, budgets, tested kill switches, and risk-scaled human review.
Review real agent failures weekly, encode improvements, and require evaluations and human approval for changes to rules, memory, or evaluations.
Design multi-agent workflows, choose appropriate human or model involvement, control loop limits in code, and separate generation from independent verification.