AceUp - Lead ML Engineer (Generative AI & LLM Focus)
About the company
AceUp is evolving from a traditional SaaS platform into an AI-first leadership development engine. We are looking for a Full Stack Engineer who excels at bridging established core systems with cutting-edge AI services. You will be the primary developer responsible for bringing our new AI capabilities "to the glass." While our ML team builds the intelligence in Python, you will build the experience. You will architect the React frontends that users interact with and the Ruby backend logic that orchestrates data between our core platform and our new AI microservices.
The Tech Stack
We are a GCP-native shop. You will be building directly within the Google Cloud ecosystem:
GenAI & Compute: Vertex AI, Gemini Pro/Ultra, PaLM API, Cloud Functions.
Data & Vector: Firestore, BigQuery, Vertex AI Vector Search.
Orchestration: Cloud Run, Pub/Sub.
Frameworks: Python, LangChain/LangGraph.
What You Will Do:
Architect Conversational Agents: Design and build stateful, context-aware conversational agents that can maintain long-running coherent dialogues, handling complex reasoning tasks rather than just single-turn Q&A.
Build RAG Pipelines: Develop low-latency retrieval systems that ground LLM responses in proprietary data, ensuring high accuracy and minimizing hallucinations.
Unstructured Data Intelligence: Lead the development of NLP pipelines to extract structured insights (semantic signals, sentiment, action items) from varied unstructured data sources (text, and eventually audio).
Personalization Architecture: Implement advanced personalization layers that dynamically adapt model behavior and tone based on user history and context.
LLMOps & Infrastructure: Own the deployment lifecycle of your models. You will be responsible for prompt architecture, evaluation frameworks, latency optimization, and cost management on Vertex AI.
Technical Mentorship: Act as the technical “North Star” for our existing ML engineers. You will review code, set architectural standards, and guide technical decision-making without the overhead of people management.
Who You Are
A “Product” Engineer: You care about the end-user experience. You don’t just optimize for accuracy; you optimize for utility, latency, and reliability in a production environment.
GCP Specialist: You are comfortable navigating the Google Cloud ecosystem and know which services to use to build scalable, secure AI solutions.
Hands-On Architect: You are looking for a role where you can code 70-80% of the time. You thrive in the IDE, not just in meetings.
Pragmatic Innovator: You stay up to date with the latest papers (LoRA, CoT, ReAct), but you know when to use a simple solution over a complex one to ship value faster.
Requirements
Experience: 6+ years of professional engineering experience, with at least 3+ years focused on ML/NLP and 1+ years specifically working with Large Language Models (LLMs) and GenAI.
Technical Fluency: Expert in Python. Strong familiarity with modern AI frameworks (LangChain, LlamaIndex) and GCP Services (Vertex AI, Firestore).
Communication: Conversational English is required. You must be able to explain complex technical trade-offs to Product Managers and Executives.
Education: B.S. or M.S. in Computer Science, Mathematics, or equivalent practical experience.
Nice to Have
Experience with Audio/Speech processing pipelines (ASR, Diarization).
Background in EdTech, HR Tech, or Psychology-based applications.
AceUp is proud to be an equal opportunity employer, seeking to create a welcoming and diverse environment. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.
Interview process:
Silver Screening interview
First meeting with client: Intro Intro Call + Problem Solving
Take Home Challenge
Technical Interview
Interview w/Product