About Full

Stack

Full

Stack is your AI-native engineering partner, built to turn AI capability into certainty.

Most companies can demo AI; few can get it to production and prove what it produced.

We close that gap with three capabilities, AI built into your products from the start, elite vetted talent who apply it, and transparent execution that shows value every step of the way.

With now over 600 customers in North America, Full

Stack is one integrated partner for all your AI engineering needs.

With Full

Stack, you move forward with confidence.

We’re Most Proud Of

- Offering life-changing career opportunities to talented software professionals across the Americas.
- Building highly-skilled software development teams for hundreds of the world’s greatest companies.
- Having delivered hundreds of successful custom software solutions, which have positively impacted the lives and careers of millions of users.
- Our 4.1-star rating on Glass

Door.
- Our client Net Promoter Score of 68, twice the industry average.

The Position

Forward Deployed Engineers sit inside the client’s problem, not next to it.

You embed with an enterprise team, find the work that actually warrants AI, architect the system, build it, and stay long enough to make it survive contact with production.

This is a three-way role: consultant, operator, engineer.

You should be as comfortable pressure-testing a business case with a COO or VP of Engineering as you are diagnosing why retrieval quality collapsed after a data refresh.

The people who succeed here can hold a discovery conversation on Monday and ship what they scoped on Thursday.

On this track, your edge is data, retrieval, and model behavior: you make agents trustworthy by getting the data, context, and evaluation right.

We work with regulated industries and Fortune 500 clients who have AI mandates, real constraints, and low tolerance for demos that don’t hold up.

How We Approach The Work

FDEs are trusted to shape the solution, not only deliver it.

Every engagement calls for architect-level judgment:

- Reframes the request.

Treats “we need an agent for X” as a hypothesis, not a spec.
- Decomposes the problem.

Outcome → process → decisions → data → systems → actors → constraints — before choosing technology.
- Chooses the intervention.

Remove, simplify, traditional software, automation, LLM, RAG, agent, or multi-agent — and defends why.
- Owns the architecture decision.

Documents trade-offs (ADRs), sets success metrics, pushes back on scope that won’t deliver value.

Consultative Discovery

Clients rarely arrive with a fully formed problem, and the FDE’s first contribution is helping them see it clearly.

You lead discovery with genuine curiosity, moving the conversation from stated requirements to underlying needs, and you build credibility through the depth of your understanding rather than the breadth of your pitch.

- Uncovers business drivers.

What outcome matters, what it’s worth, why now, and what happens if nothing changes.
- Uncovers personal drivers.

What each stakeholder is measured on, what they’re worried about,



and what a win looks like for them individually.
- Surfaces constraints early.

Security, compliance, data access, budget, skills, politics, timelines — the things that kill projects in month two.
- Aligns stakeholders.

Spots conflicting goals between business, engineering, and risk, and brings them to a shared definition of success.
- Asks strategic questions.

Follows the answer with “why,” “how do you know,” and “what happens today when…” — without interrogating.

Clients leave the conversation understanding their own problem better.
- Earns authority through depth.

Credibility comes from the quality of the questions and the insight in the playback, not from pitching technology.

What You'll Do

- Discover.

Run workshops and process discovery with business, data, and engineering stakeholders.

Assess data readiness honestly.

Separate genuine AI problems from workflow problems wearing an AI costume.
- Structure the process and the spec.

For any business process being automated, map the current state, decision points, data inputs, and exceptions, then turn it into specifications that both people and AI systems can execute against reliably.
- Architect & design.

Design agentic systems end to end with emphasis on context and retrieval architecture — data sourcing, chunking, embeddings, hybrid search, reranking, permissions-aware retrieval, knowledge graphs where warranted — plus orchestration, MCP/tool integration, guardrails, and human-in-the-loop checkpoints.
- Build.

Ship working systems — agents, retrieval services, evaluation tooling, and the data flows that feed them.

Prototypes that prove value in weeks, not slideware.
- Engineer the delivery system.

Own CI/CD for what you build, extended to AI: prompt and context versioning, eval suites that run in the pipeline and gate releases.
- Operate (Day 2).

Own what happens after launch — eval harnesses, groundedness and retrieval metrics, observability and tracing, cost and latency management, drift, and the handoff that lets the client’s team run it without you.
- Consult.

Present to and defend decisions in front of CTOs, VPs of Engineering, CDOs, and business leadership.

Quantify impact in their terms.

Support pre-sales scoping and proposal work when the deal calls for it.

What We're Looking For

Engineering foundation

- 8–10+ years across ML, data, and software engineering.
- Python-first, production-grade (typed, tested, deployed services — not notebooks only); working proficiency in Type

Script / Node, .NET / C#, or Java is a plus.
- Track record as an ML/Data Architect, Principal Engineer, or Tech Lead — can assess a data and AI current state and define a credible roadmap.
- Has designed and owned CI/CD for AI or ML systems (LLMOps / MLOps),



not just application code handed off to others.

Applied / operational AI

- Has put LLM and agent systems into production and kept them running — not just proofs of concept or research models.
- Deep fluency with retrieval and model behavior: embeddings, vector and hybrid search, reranking, structured output, model selection and routing; sound judgment on fine-tuning vs. prompting vs. retrieval.
- Fluent with agentic tooling: Claude Code, agent frameworks, MCP, tool/function calling, multi-agent orchestration.
- Context and spec engineering: designs what the model sees — retrieval, context construction, specifications, process definitions, and memory — for reliable, context-efficient output.
- Day 2 experience: evaluation design (golden sets, LLM-as-judge, groundedness), observability, cost and token economics, failure-mode analysis.
- Data and integration literacy: familiar with ETL/ELT patterns, warehouses/lakehouses (Snowflake, Databricks, Big

Query), and integration options (APIs, event streams, iPaaS, MCP) — enough to design around them, assess data readiness, and guide the data team.

Does not need to be an ETL/ELT specialist.

Consulting & business

- Demonstrates the discovery behaviors above: business and personal drivers, constraints, stakeholder alignment, strategic questioning.
- Runs client conversations without an account manager in the room: workshops, proposal walkthroughs, hard questions, pushback.
- Translates technical decisions into business outcomes — accuracy, cycle time, cost per transaction, risk reduction, ROI.
- Exceptional written and verbal communication; credible in front of senior stakeholders.

Strong Plus

- Delivery in regulated environments (financial services, healthcare, insurance, manufacturing); data governance and privacy experience.
- Pre-sales support: discovery calls, SOW and estimate shaping, technical proposal defense.
- Classical ML in production (forecasting, classification, anomaly detection) alongside LLM work.
- Client enablement and training — building the client team’s capability, not just the system.
- Prior consulting or agency environment; comfort with ambiguity and multiple accounts.

Work Authorization Requirements

- Applicants must be currently authorized to work in the country of this job posting on a full-time basis.
- Full

Stack will not sponsor applicants for work visas now or in the future.

What We Offer

- Competitive pay.
- 100% remote work.
- The ability to work with leading startups and Fortune 500 companies.
- Continuing education opportunities.
- Opportunity to grow and expand your career.

Our talent network is committed to fostering a diverse, inclusive, and accessible environment where IT professionals can find opportunities that match their skills.

We welcome individuals of all races, religions, genders, sexual orientations, national origins, abilities, and experiences.

Our focus is on connecting talent with projects based on qualifications, ensuring a fair and transparent process that values diverse perspectives and general collaboration.

Learn more about our Applicants Privacy Notice.

📌 Forward Deployed AI Engineer (Python) - Remote -Latin America (Cusco)
🏢 FullStack
📍 Perú