AI Engineer, Biggy Engineering

What You’ll Do
As an AI Engineer on Biggy Engineering, you will design, build, and ship AI-powered product capabilities across the Biggy platform. You will own meaningful features end to end: understand the customer problem, shape the solution, make technical decisions, build the system, validate it hands-on, launch it, respond to issues, and iterate quickly.
You will work on systems involving LLMs, agents, RAG, knowledge graphs, vector databases, workflow orchestration, enterprise integrations, automation, web applications, APIs, Slack and Microsoft Teams experiences, and operational data pipelines.

You will be expected to maximize velocity and output without becoming reckless. The team values engineers who take problems head-on, unblock themselves, communicate clearly, and push through ambiguity rather than waiting for perfect requirements or ideal conditions. You will also be expected to share best practices and contribute where you see opportunities to enhance how the team as a whole operates to meet our goals.

While the role is centered on engineering execution, our core operating philosophy prioritizes complete product success above all else. Every team member meets with and builds relationships with customers, develops their own ideas, and shares ownership of our strategic roadmap - actively shaping what we ought to construct and driving technical choices that directly influence the customer experience with Biggy.

Responsibilities
Build production-grade software across backend services, APIs, web applications, workflow systems, AI agents, enterprise integrations, and automation platforms.
Architect and implement AI-native capabilities using LLMs, prompting, tool calling, agent orchestration, RAG, vector search, knowledge graphs, embeddings, and structured/unstructured enterprise data.
Use AI coding platforms such as Cursor, Claude Code, and similar tools as a core part of your development workflow to increase speed, exploration, and delivery quality.
Write high-quality prompts for development, debugging, product behavior, agent execution, data extraction, reasoning workflows, and customer-facing AI experiences.
Propose, design and own features from concept through delivery, including customer discovery, technical design, implementation, manual validation, release, bug resolution, and iteration.
Work directly and build relationships with customers, GTM, product, and other engineers to identify high-value problems and translate them into product capabilities.
Make pragmatic tradeoffs between speed, quality, reliability, cost, latency, scalability, and customer impact.
Move fast in ambiguous problem spaces without hiding behind process, excessive documentation, or prolonged design debates.
Respond rapidly and transparently to bugs, regressions, and customer-impacting issues.
Collaborate through paired development, design discussion, code review, debugging, and direct technical debate.
Help raise the team’s bar for velocity, technical judgment, ownership, and customer-focused execution- building and contributing to internal tools and processes within and outside of our team.

What We’re Looking For
Strong software engineering fundamentals across backend, full-stack, distributed systems, APIs, or product engineering.
Hands-on experience building AI-enabled product capabilities with LLMs, RAG, vector databases, embeddings, agents, workflow automation, knowledge systems, or related technologies.
High agency: you learn what you need to learn quickly, make forward progress independently, and do not wait for someone else to define every step.You want to develop ideas independently to improve customer outcomes and team operations.
Extreme ownership: you care about outcomes, customer impact, reliability, and follow-through, not just completing assigned tickets.
High velocity: you are motivated to ship, learn, and iterate quickly while maintaining sound engineering judgment.
Customer curiosity: you want direct exposure to users and are comfortable using customer feedback to shape priorities.
Product judgment: you can distinguish between what is technically interesting and what creates meaningful customer value.
Strong practical prompting skills, including prompt iteration, context design, tool-use instructions, structured outputs, and failure-mode analysis.
High proficiency with modern AI development tools such as Cursor, Claude Code, ChatGPT, or similar platforms; you should already be using AI to materially improve your engineering output.
Ability to architect and build agent systems that can reason, retrieve context, call tools, execute actions, handle errors, and operate safely in enterprise environments.
Healthy conflict: you can challenge weak ideas directly while remaining considerate, collaborative, and low-ego.
Comfort with uncertainty, changing priorities, incomplete requirements, and fast iteration cycles.
Excitement about applying AI to real enterprise IT Operations problems, not just building demos.

Technical Areas You May Work In Already
(You do not need to be familiar with all of these)
Extremely familiar with agentic coding (Cursor, Claude Code, Codex etc.)
TypeScript / Node.js backend services
ITOps domain expertise: change risk, ITSM, and incident prevention use cases
React / Next.js customer-facing applications
Slack and Microsoft Teams applications
Agent workflow engines, Agent-tracing, Langsmith etc.
RAG pipelines, vector databases, embeddings, and retrieval systems
Knowledge graphs, agent memory, and enterprise knowledge modeling
Prompt engineering, context engineering, and tool-use design
ServiceNow, ITSM, monitoring, alerting, and incident management integrations
Data ingestion, indexing, synchronization, and unstructured data processing
S3, SQS, Lambda, and AWS-based service architecture
Authentication, authorization, OAuth, SSO, API keys, and RBAC

You’ll Thrive Here If
You are a builder with urgency and agency. You do not hesitate when faced with ambiguity, hard problems, unfamiliar technology, or customer pressure. You take things head-on, figure out what needs to be learned, use every tool available to move faster, and drive the work to completion. You figure out what we should be building and how we should be operating, you are not simply assigned tasks.
You are comfortable building a strong MVP quickly, putting it in front of users, learning from feedback, and improving it. You care about architecture, but you do not use architecture as an excuse to slow down. You value direct communication, high standards, fast feedback loops, and meaningful customer impact.

This May Not Be the Right Role If
You need fully defined requirements before starting, prefer long design cycles before implementation, are uncomfortable with direct customer interaction, or do not want AI tools deeply embedded in your daily engineering workflow. It may also not be a fit if you prefer narrow ownership, low ambiguity, low conflict, or optimizing mature systems more than inventing new product capabilities.

Why This Role Matters
Biggy is one of BigPanda’s most strategic product initiatives. We are building AI capabilities that change how enterprise IT Operations teams understand and investigate incidents, prevent outages, automate workflows, and make operational decisions. A strong AI Engineer on this team will directly influence product direction, customer outcomes, engineering velocity, and the pace at which BigPanda brings differentiated AI capabilities to market.