Job Description
This is a remote position.
REMOTE / WORK FROM HOME POSITION
Before applying, please note:
The entire application process must be completed in English. Fluent written and spoken English is required and will be evaluated.
Applicants must be independent freelancers, not represented by another agency.
Purpose
The AI Engineer will own the design and implementation of the company's next AI product, building agent-native platforms powered by multi-agent orchestration, Retrieval-Augmented Generation (RAG) and production-grade machine learning pipelines deployed on Databricks.
Definitions & Abbreviations
AI Agent: An autonomous software component powered by a Large Language Model that reasons over a goal and executes actions through a defined tool-use interface.
Orchestration Layer: The software boundary that coordinates multiple agents, tools, memory and control flow across an AI workflow, commonly implemented with frameworks such as LangGraph or LangChain.
RAG (Retrieval-Augmented Generation): An architecture pattern where an LLM is grounded on external knowledge retrieved from a vector store or domain-specific catalog before generating a response.
MLOps: The set of practices for building, deploying, monitoring and iterating machine learning models in production, typically covering experiment tracking, CI/CD and model governance.
NLP (Natural Language Processing): The field of techniques for programmatically understanding, classifying and generating human language.
LLM (Large Language Model): A foundation model trained on broad corpora and fine-tuned to follow instructions, used as the reasoning engine of an agent.
Role Overview
Mission
Lead the technical build of Fountain Forward's first agent-native AI product, from prototype to production.
Design and ship a robust orchestration layer that lets multi-agent workflows autonomously plan, retrieve, reason and act over enterprise data.
Partner directly with product, data and client-facing teams to translate business problems into reliable AI capabilities.
Scope of Impact
This is a builder role, not a research role: the work is measured by features shipped, uptime and business outcomes.
The AI Engineer is expected to own the full stack of an AI feature, including agent design, tool interfaces, retrieval pipelines, model fine-tuning when relevant, and the data models that feed them.
Key Responsibilities
Agent Development and Orchestration
Design, build and maintain multi-agent workflows using LangGraph, LangChain and comparable orchestration frameworks.
Implement typed, well-documented tool-use layers that expose backend capabilities (APIs, databases, ML models) to agents in a safe and auditable way.
Build RAG systems over domain-specific catalogs, including chunking strategy, embedding model selection, vector store management and retrieval evaluation.
Instrument agent runs with tracing, evaluation harnesses and regression suites to keep quality measurable as the system evolves.
Data Modeling on Databricks
Design and implement the data models that back the AI product on Databricks, using a medallion (bronze, silver, gold) approach where applicable.
Build scalable ETL pipelines that ingest from SharePoint, cloud storage, SaaS APIs and client systems into curated Delta tables consumable by agents and ML models.
Apply data quality, schema validation and lineage controls so that every AI output can be traced back to a trustworthy source.
Collaborate with data analytics to expose governed datasets for downstream reporting, including Power BI consumption layers.
Machine Learning as Tools for Agents
Develop, train and deploy machine learning models (classification, scoring, forecasting, NLP) that agents invoke as tools inside the orchestration layer.
Wrap models behind clean, typed endpoints with predictable contracts so they can be composed by agents and other services.
Maintain a disciplined MLOps workflow using MLflow for experiment tracking and CI/CD pipelines (Azure Pipelines, GitHub Actions or GitLab CI) for promotion to production.
Monitor deployed models for drift, latency and cost, and iterate based on real user feedback.
Collaboration and Delivery
Work closely with the founding team to translate ambiguous product bets into concrete technical plans with realistic milestones.
Write clean, reviewable code, document architectural decisions and participate actively in code reviews.
Communicate trade-offs (accuracy vs. cost vs. latency vs. complexity) in language that both engineers and business stakeholders can act on.
Requirements
Required Qualifications
Education
Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Software Engineering or a closely related engineering discipline.
Coursework or demonstrated depth in at least two of: machine learning, data structures and algorithms, databases, cloud computing, MLOps.
Professional Experience
At least one (1) year of professional experience as a Data Engineer, AI Engineer, ML Engineer or equivalent applied role.
Verifiable, hands-on experience shipping at least one AI or data product to production (not just notebooks or coursework).
Experience building or contributing to agent-based systems with tool use, RAG or multi-step reasoning workflows.
Core Technical Requirements
Advanced Python, including typed code, testing and packaging.
Strong SQL and practical experience modeling data on Databricks (Delta Lake, Unity Catalog, Spark).
Production experience with LangChain and LangGraph, or a clearly equivalent agent-orchestration framework.
Experience building and deploying machine learning models that are consumed as tools, services or scheduled jobs.
Comfortable working on a cloud platform (Azure preferred, AWS or GCP acceptable) and with Git-based workflows.
Preferred Qualifications
High-Signal Nice-to-Haves
Experience designing stateless, explainable scoring or decision engines whose outputs can be audited factor by factor.
Experience building multi-tenant or multi-entity data frameworks with automatic schema detection and entity-specific business rules.
Experience with MLflow, Azure Pipelines and Power BI as part of an end-to-end MLOps and reporting stack.
Experience building full-stack services (e.g. FastAPI or Django backends, Next.js or similar frontends, containerized with Docker) to expose AI capabilities.
Exposure to NLP techniques (embeddings, classification, topic modeling) and to at least one deep learning framework such as PyTorch, TensorFlow or Keras.
Published research, conference presentations or open-source contributions related to AI, data science or agent systems.
Prior internship or early-career recognition (awards, honors, distinguished-student status) that signals consistent high performance.
Soft Skills We Care About
Strong written and spoken English (B2 or higher). Additional languages are a plus.
High ownership: comfort moving from a vague brief to a shipped feature with limited supervision.
Clear, respectful communication with non-technical stakeholders.
Genuine curiosity about the agentic AI landscape and a habit of tracking how the frontier is moving.
Technical Skills Summary
Must Have
Languages and data: Python (advanced), SQL, Spark.
Agent stack: LangChain, LangGraph, RAG patterns, vector stores.
Data and cloud: Databricks, Azure, Delta Lake, Git, CI/CD pipelines.
ML and MLOps: scikit-learn or equivalent, MLflow, model deployment.
Strongly Preferred
Docker, FastAPI or Django, Next.js or a modern frontend framework.
Power BI or Tableau for governed reporting on top of curated data.
NLP tooling (NLTK, spaCy, Hugging Face), PyTorch, TensorFlow, Keras.
Apache Airflow or equivalent orchestrator, DBT for transformations, Linux.
Experience integrating external APIs (travel, hospitality, maps, enterprise SaaS).
Benefits
How We Work
Freedom and flexibility. We’re a team working from around the world. That is, as long as they show up on our weekly calls, do their job correctly, and have a good sense of accountability.
Autonomy and ownership. Working on a distributed team means you don’t have someone micromanaging you or looking over your shoulder to make sure you’re getting things done. We’re a team of do-ers who take full ownership for their results.
Be helpful and dedicated. We help our clients be successful. We help our prospects get the right information and make the right decision whether or not it includes our services. We help our team members reach their full potential.
The Perks!
Remote-First Culture: Work from home—or anywhere in the world. We value results, not where you log in from.
Flexible Schedule: We offer flexibility in your daily schedule (within reason) to help you do your best work while maintaining a healthy work-life balance.
Unlimited Paid Time Off: Enjoy an unlimited vacation policy with just two weeks’ notice and manager approval. We also offer paid sick days because your well-being matters.
Parental Leave: We understand the importance of supporting our team members during significant life events. Our maternity and paternity leave policy ensures you can prioritize your growing family without added stress.
Room to Grow: We’re a fast-growing company with massive opportunities for upward mobility. As we grow, you grow with us.
Professional Development: Get access to training in digital marketing, advertising, and leadership to sharpen your skills and advance your career.
Quarterly Performance Bonuses: Earn bonuses based on your performance and contributions to the company’s success.
Paid Referral Program: Earn rewards for referring great talent to join our team!
Health & Fitness Reimbursement (upon tenure): Get support for health insurance or a gym membership—because your well-being matters.