Founding AI/ML Engineer

About Us

At ProfoundIQ, we provide Fractional CTO services and hands-on technical leadership to startups and growing businesses. Our goal is to help founders make confident technology decisions, accelerate product development, and build engineering teams capable of supporting long-term scale. We partner with companies at various stages of growth, from early product validation to scaling platforms.

Through our CTO-as-a-Service model, we combine strategic guidance with strong engineering execution. Our internal engineering team plays a key role in this model by working directly on client products and contributing hands-on expertise in architecture, development, and delivery. You will be part of a team that supports multiple clients across diverse industries, solving real-world technical challenges and helping bring high-quality products to market.

Role Overview

We are looking for a Founding AI/ML Engineer to lead the development of core intelligence systems across understanding, reasoning, workflow automation, and agent orchestration. This is a high-ownership role ideal for someone who enjoys building foundational AI capabilities from the ground up.

The role involves designing, training, and deploying end-to-end ML/LLM systems. You will work across multi-channel content understanding, task inference, work decomposition, agent routing, retrieval pipelines, and building reliable, low-latency AI components.

Key Responsibilties

Cross-Channel Communication Understanding

Build systems that understand and classify content across Email, Slack, issue trackers, and

documents into tasks, status checks, meeting requests, information, or noise.

Task Domain Identification

Determine the domain of each task (engineering, design, product, writing, support, etc.) -

based on message content + context.

Work Decomposition - Infer the actual work needed

Take a vague task request and convert it into structured, actionable work units.

Agent Routing & Orchestration

Once the work is known, trigger internal or external agents that can help complete it, with

human confirmation where required.

Project/Topic Mapping

Cluster related messages across channels into implicit “Projects” without teams needing to

create them manually.

Autonomous fetch of latest Status

For messages asking “Where does this stand?”, pull data from Slack threads, Jira/Linear, Git/

GitHub/Bitbucket, and prepare an accurate update for user approval.

Scheduling Intelligence

Detect meeting-related asks, identify participants, find availability, and propose times.

Technical Focus Areas

* LLM reasoning in messy real-world environments

* RAG across live organizational systems

* Dynamic agent orchestration

* High-bar product reliability

* User-in-the-loop workflows

* Execution-critical latency constraints

You’ll be building the core intelligence of the product, not supporting it.


Required Skills

* You’ve built and shipped ML/LLM systems in production.

* You can own data → modeling → evaluation → infra → deployment.

* You think in terms of reliability, precision/recall, and trust.

* You prefer small teams and fast iterations.

* You want to build something the best global teams rely on every single day.

Good to Have

* Experience with agentic/automation frameworks (LangChain, LlamaIndex, custom agent systems)

* Experience integrating Slack, Email, Jira, GitHub

* Prior early-stage or founding-engineer experience

What This Role Offers

* High autonomy and end-to-end ownership

* Opportunity to architect mission-critical AI systems

* Fast-paced environment with meaningful impact

* Significant influence on technical direction and execution

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