Solutions Engineer - AI Focus
Job description
Solutions Engineer (AgentControl) at LaunchDarkly.
About the role
This position delivers technical validation and market adoption for AgentControl, shaping how AI engineers build and deploy agents.
Software engineers turn product ideas into working code. Engineers work in small teams, review each other's work, and ship in small batches. Most teams follow agile practices such as sprints and daily standups. Engineers also write tests, fix bugs, and improve performance. The field values clear communication as much as technical skill. Engineers spend part of every week on planning, code review, and debugging, not just writing new code. The ability to explain a technical decision in plain words separates strong engineers from the rest.
Key facts
What you'll do
Customer conversations gain practical, credible AI applications expertise that shapes solutions and validates technical win.
Technical wins are validated and revenue is secured from the largest customers through AgentControl POV leadership.
Product concepts confirm technical feasibility and align with LaunchDarkly strategic goals via extensive collaboration with product and engineering teams.
Field Team capability on AgentControl is enabled through collaboration and communication of capabilities and value.
Competitor playbooks are built and maintained through collaboration with AI Researchers, PMM, and AI Strategy.
Requirements
The posting states a bachelor's degree requirement. Extensive experience with AI applications including building, implementing, or selling AI solutions at scale is required.
Experience building multi-agent systems using frameworks such as LangGraph, AgentBuilder, or AgentCore is required.
Hands-on experience evaluating AI agent performance at scale using automated evaluation methods is required.
A deep understanding of LLM mechanics, including the ability to explain transformer architecture in detail, is required.
Experience building or interfacing with MCP (Model Context Protocol) servers is required.
Strong Python skills with experience building in PyTorch or TensorFlow are required.
A strong foundation in software engineering principles and current market trends is required.
A minimum of 12 years of related experience is required.
Nice to have
Holding strong but loosely-held opinions about AI with the ability to update views based on evidence is beneficial.
Anticipating where the AI landscape is heading and positioning products accordingly is valuable.
Maintaining deep curiosity about how AI changes software development, with an obsession for staying current on new AI technology, is helpful.
Natural storytelling ability to craft compelling narratives for technical audiences is an advantage.
Thriving in ambiguity, with a love for figuring things out, building new processes, and working in undefined spaces, is preferred.
Skills & tools
Familiarity with AI agent frameworks, evaluation methods, and model context protocols supports effective execution.
Proficiency in Python, PyTorch, and TensorFlow enables AI application development.
Experience contributing to open source and delivering technical content aids collaboration and communication.
Practical notes
This role requires a degree.
The expected work location is Remote within the United States.
Compensation varies by geographic zone and individual skills and experience.
Typical interview steps
Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.
Good to know
The role centers on AI agent lifecycle management and go-to-market validation.
Expect to work with modern AI stacks including large language models and agent frameworks.
Public speaking and narrative building influence technical audiences effectively.
Cross-functional collaboration with research, product marketing, and strategy teams is central.
The position involves both solution design and sales engineering responsibilities.