Gen AI Engineer

Open Data Science
London
4 days ago
Applications closed

Related Jobs

View all jobs

AI Engineer - Jan 25

AI Engineer

Senior Software Engineer in Product

Software Engineer - C# / Design and Simulation

Senior System Engineer-Mechanical

Data Engineering Consultant

LangChain CrewAI AutoGen GenAI LLM NLP Startup Transformers GetThingsDone AI

Brief description of the vacancy

We’re looking for a Gen AI Scientist to develop, scale, and support our LLM-driven autonomous platform. You’ll work with LangChain, AutoGen, CrewAI, and deploy open-source models (LLaMA, DeepSeek) in the Google Cloud.

About the company

Anecdote is an innovative, AI-first startup revolutionizing how companies analyze customer feedback. Our AI-powered platform consolidates feedback from app reviews, support chats, surveys, and social media into a single, easily accessible space. This enables companies like Grubhub, Dropbox, and Careem to derive actionable insights and deliver a better, real-time customer experience that drives sustainable growth.

We are backed by top investors, including Neo, Sukna, Race Capital, Propeller, and Wamda, having raised $3.5m to date.

Responsibilities

  • Develop, scale, and support our LLM agentic system platform.
  • Design and implement AI-driven autonomous workflows, enabling seamless human-AI interaction.
  • Build and deploy open-source models in cloud environments, optimizing inference and serving costs.
  • Improve and maintain data pipeline reliability and participate in on-call rotations.
  • Debug and fix issues in ML pipelines, even when the cause is obscure.
  • Collaborate with cross-functional teams to integrate AI models into production systems.
  • Clearly articulate the work you’ve done and the impact you’ve made.

We are early stage, so the work is dynamic and evolving.Examples of additional challenges you might tackle:

  • Make things work. Even the hardest things.
  • Deploy AI models in scalable and cost-efficient ways.
  • Optimize prompts, refine model outputs, and experiment with novel prompting strategies.
  • Implement backend endpoints to bridge AI capabilities into our production stack.
  • Label data and refine model training workflows.
  • Hire and manage part-time annotators to improve data quality.
  • Create quick prototypes using Dash/Streamlit to validate concepts.
  • Own features end-to-end, from ideation to deployment.
  • Be on-call for urgent AI model fixes or system failures.

Qualifications

  • Proficiency in Python and related libraries (e.g., NumPy, SciPy, pandas) is required.
  • Strong production experience with at least one framework: LangChain, AutoGen, or CrewAI.
  • Deep understanding of agentic systems, autonomous workflows, and LLM-based automation.
  • Experience deploying and fine-tuning open-source models (e.g., LLaMA, DeepSeek) in the cloud.
  • 5 years of hands-on experience in building, productionizing, iterating, and scaling AI-driven pipelines.
  • Ability to take projects to completion, unblock yourself, and present results clearly and impactfully.
  • Staying on top of recent trends, with hands-on experience in fine-tuning LLMs beyond API comparisons.
  • Strong knowledge of software engineering, including building scalable web services and APIs. Experience developing full-stack applications, including database design, API development, admin panel creation, and monitoring systems.
  • Experience with GCP is a big plus.
  • DevOps experience is a big plus.
  • Prompt engineering expertise and creative problem-solving mindset.
  • Experience with processing multimodal data (text, images, audio) is a plus.

Perks and Benefits:

  • Fully Remote:Work from anywhere with flexible hours.
  • In-person Meetups and regular team-building remote events:Enjoy occasional meetups and monthly game sessions.
  • Generous Vacation:Take time off when you need it.
  • Growth Opportunities:Continuous professional development and learning support.
  • Dynamic Culture:Be part of a fast-moving, high-impact team.
  • Stock Options:Get equity in our growing startup.

Contacts

Log In Only registered users can open employer contacts.

J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Quantexa Machine‑Learning Jobs in 2025: Your Complete UK Guide to Joining the Decision‑Intelligence Revolution

Money‑laundering rings, sanctioned entities, synthetic identities—complex risks hide in plain sight inside data. Quantexa, a London‑born scale‑up now valued at US $2.2 bn (Series F, August 2024), solves that problem with contextual decision‑intelligence (DI): graph analytics, entity resolution and machine learning stitched into a single platform. Banks, insurers, telecoms and governments from HSBC to HMRC use Quantexa to spot fraud, combat financial crime and optimise customer engagement. With the launch of Quantexa AI Studio in February 2025—bringing generative AI co‑pilots and large‑scale Graph Neural Networks (GNNs) to the platform—the company is hiring at record pace. The Quantexa careers portal lists 450+ open roles worldwide, over 220 in the UK across data science, software engineering, ML Ops and client delivery. Whether you are a graduate data scientist fluent in Python, a Scala veteran who loves Spark or a solutions architect who can turn messy data into knowledge graphs, this guide explains how to land a Quantexa machine‑learning job in 2025.

Machine Learning vs. Deep Learning vs. MLOps Jobs: Which Path Should You Choose?

Machine Learning (ML) continues to transform how businesses operate, from personalised product recommendations to automated fraud detection. As ML adoption accelerates in nearly every industry—finance, healthcare, retail, automotive, and beyond—the demand for professionals with specialised ML skills is surging. Yet as you browse Machine Learning jobs on www.machinelearningjobs.co.uk, you may encounter multiple sub-disciplines, such as Deep Learning and MLOps. Each of these fields offers unique challenges, requires a distinct skill set, and can lead to a rewarding career path. So how do Machine Learning, Deep Learning, and MLOps differ? And which area best aligns with your talents and aspirations? This comprehensive guide will define each field, highlight overlaps and differences, discuss salary ranges and typical responsibilities, and explore real-world examples. By the end, you’ll have a clearer vision of which career track suits you—whether you prefer building foundational ML models, pushing the boundaries of neural network performance, or orchestrating robust ML pipelines at scale.

Machine Learning Programming Languages for Job Seekers: Which Should You Learn First to Launch Your ML Career?

Machine learning has swiftly become a cornerstone of modern technology, transforming entire industries—healthcare, finance, e-commerce, and beyond. As a result, demand for machine learning engineers, data scientists, and ML researchers continues to surge, creating a rich landscape of opportunity for job seekers. But if you’re new to the field—or even an experienced developer aiming to transition—the question arises: Which programming language should you learn first for a successful machine learning career? From Python and R to Scala, Java, C++, and Julia, the array of choices can feel overwhelming. Each language boasts its own community, tooling ecosystem, and industry use cases. This detailed guide, crafted for www.machinelearningjobs.co.uk, will help you align your learning path with in-demand machine learning roles. We’ll delve into the pros, cons, and ideal use cases for each language, offer a simple starter project to solidify your skills, and provide tips for leveraging the ML community and job market. By the end, you’ll have the insights you need to confidently pick a language that catapults your machine learning career to new heights.