Full time

Callière Group; Chicago (IL); On-site; USD 110,000 - 150,000; Full time

Job summary

Callière Group is seeking an Intermediate Client-Embedded AI Solutions Engineer to work embedded inside client organizations in Chicago. You will ship integrations, own workstreams, and help production‑grade systems meet adoption targets while collaborating with platform engineers and a senior counterpart.

The role blends hands‑on technical delivery with a strong focus on how client teams actually work, aiming to deliver measurable impact and reusable patterns across engagements.

Qualifications

• 2–4 years shipping software or workflow automation in production.
• Strong solution architecture with APIs, integrations, data contracts.
• Production‑reliability mindset baked into development.
• Experience designing AI‑enabled workflows: prompting and model selection.
• Strong technical writing and ability to document specs and runbooks.
• Comfort working inside client organizations at team level.

Responsibilities

• Join discovery sessions to map workflows and constraints.
• Design and build AI‑driven workflows with prompts and grounding.
• Deliver production‑grade prototypes with proper logging and error handling.
• Define acceptance criteria, KPIs, and monitoring plans.
• Handle sensitive data per security requirements.
• Collaborate with platform engineers and senior counterparts on integration design.
• Build trust with client teams and contribute reusable patterns.

Skills

Shipping software

Solution architecture

Reliability engineering

AI workflows design

Technical writing

Client immersion

Problem solving

Tools

APIs

Integrations

Job description

Intermediate Client-Embedded AI Solutions Engineer (aka: FDE)

Chicago, United States | Posted on 08/19/2026

We operate at the intersection of AI consulting, venture building, and private equity; a firm designed so that operational work in client environments feeds directly into new venture creation and acquisition strategy, and vice versa. Rather than treating advisory work, startup building, and investment as separate businesses, we run them as one connected system: lessons learned deploying AI inside client organizations shape which companies we spin up, which existing playbooks we look to acquire, and how our internal platform evolves. The intent is to give people a mix most career paths don’t offer: steady compensation alongside multiple forms of long-term equity upside (platform, venture, and fund-level).

The Role

We’re hiring a mid-level engineer to work embedded inside client organizations as part of a small delivery team. You’ll sit close to the actual business problem. Not just design a solution on paper, but build and ship the integration yourself. This role blends hands‑on technical delivery with genuine attention to how the client’s teams actually work day to day.

You’ll work alongside a more senior embedded engineer, an engagement lead who owns the client relationship, and platform engineers who build the underlying capabilities your integrations rely on. At this level, you’ll get architectural guidance from a senior teammate but will independently own specific workstreams by shipping features, hardening systems for production, and helping the broader engagement hit its adoption targets.

What Success Looks Like

• Integrations and automations that are actually running in production, not just proposed
• Assigned workstreams delivered on schedule and meeting agreed acceptance criteria
• Production systems with measurable operational impact — time saved, fewer errors, higher throughput
• Reusable components or patterns from your work that others on the team can build on
• Documentation, runbooks, and monitoring thorough enough that someone else could maintain what you built

Core Responsibilities

• Join discovery sessions inside client environments to map current workflows, understand existing tooling, and surface real constraints
• Design and build AI‑driven workflows — prompt design, retrieval/grounding approaches, choosing models and providers, and putting guardrails and fallback logic in place
• Bring prototypes to production‑grade quality: error handling, retry logic, idempotency, logging, monitoring, and access control
• Build against clearly defined acceptance criteria, KPIs, monitoring plans, and rollback procedures
• Handle sensitive data (secrets, PII) in line with client and internal security requirements
• Make and document scoped technical tradeoffs, escalating bigger architectural decisions upward
• Collaborate closely with platform engineers on extending shared tooling, and with your senior counterpart on integration design
• Build trust directly with client working teams
• Feed reusable patterns from client work back into the broader platform

How We Work

We move fast toward clarity by defining the problem, the metric that matters, and the next concrete step. We’d rather ship something real than debate it in a meeting. We do the unglamorous reliability work most teams skip. We care more about preventing failures than firefighting them, and we stay curious about what AI can do while staying grounded about what it can’t, yet.

Requirements

What We’re Looking For

• 2–4 years shipping software or workflow automation systems that reached real production use
• Solid grasp of solution architecture: APIs, integrations, data contracts, auth/permissions, and reliability practices
• A production‑reliability mindset baked into how you build; not an afterthought
• Practical experience designing AI‑enabled workflows: prompting, retrieval, model selection, guardrails
• Good judgment under ambiguity and time pressure; you make calls within your scope and know when to elevate
• Strong technical writing; specs, interface contracts, runbooks
• A problem‑solver's instinct paired with empathy for how the people using your systems actually work
• Comfortable operating inside client organizations at the working‑team level

Nice to Have

• Experience in a client‑embedded or forward‑deployed technical role (consulting‑engineering backgrounds welcome)
• Hands‑on work with LLM or agent‑based system architectures
• Background automating operations inside consulting or professional‑services firms
• Experience integrating enterprise systems (CRM, ERP, ITSM, HRIS) via API
• Familiarity with process‑mapping and operational design methods