FullStack
Forward Deployed AI Engineer (Python) - Remote -Latin America
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About FullStack
FullStack 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, FullStack is one integrated partner for all your AI engineering needs. With FullStack, 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 GlassDoor.
- 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.
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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
- 5+ years of professional software development experience with at least one mainstream production stack (Python, TypeScript/Node.js, Java, C#/.NET, or Go).
- 1 - 4 years of professional experience shipping agentic AI solutions, LLM workflows, and RAG architectures in production environments.
- Advanced English is required.
- Successful completion of a four-year college degree is required.
- Hands-on experience with frontier AI developer tools including Cursor, Claude Code, Gemini CLI, or OpenAI Codex.
- Track record of leading client-facing discovery calls, asking "why" before "how" to decompose unstructured business problems.
- Solid understanding of architecture paradigms such as RESTful APIs, microservices, and event-driven systems.
- Well-versed in agentic orchestration tools and frameworks like LangGraph, LangChain, N8N, or direct Model Context Protocol (MCP) and LLM APIs.
- Skilled at exercising end-to-end delivery ownership and managing project scope without requiring product manager supervision.
- Ability to work through new and difficult issues and contribute to libraries as needed.
- Ability to create and maintain continuous integration and delivery of applications.
- Forensic attention to detail.
- A positive mindset and a can-do attitude.
- Experience working on Agile / Scrum teams.
- Meaningful experience working on large, complex systems.
- Ability to take extreme ownership over your work. Every day is a challenge to ensure you are performing to the expectations you and your team have agreed upon.
- Ability to identify with the goals of FullStack's clients, and dedicate yourself to delivering on the commitments you and your team make to them.
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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. FullStack 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 global collaboration.
Learn more about our Applicants Privacy Notice.
Nice To Have
We’re Seeking Engineers Who Are AI-enthusiastic In Their Workflow. Professional Experience Or Personal Project Work With The Following Is a Significant Plus:
- Cursor, Windsurf, or Google Antigravity.
- Claude Code, Gemini CLI, Codex, or Copilot CLI.
What We Are Looking For
- 5+ years of professional experience as a Machine Learning Engineer with strong knowledge of AI.
- Advanced English is required.
- Design, develop, and test machine learning algorithms and models.
- Analyze and interpret large amounts of data to identify patterns and trends.
- Implement and optimize models in production environments.
- Develop and maintain documentation on machine learning models and processes.
- Identify and address issues that may arise during development and deployment.
- Knowledge of statistical modeling and inference techniques.
- Successful completion of a four-year college degree is required.
- Experience working on Agile / Scrum teams.
- Forensic attention to detail.
- A positive mindset and a can-do attitude.
- Ability to identify with the goals of FullStack's clients, and dedicate yourself to delivering on the commitments you and your team make to them.
- Ability to consistently work 40 hours per week.
Work Authorization Requirements
Applicants must be currently authorized to work in the country of this job posting on a full-time basis. FullStack will not sponsor applicants for work visas now or in the future.
The Opportunity
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