Machine Learning Engineer

Grow with us. We are looking for a Machine LearningEngineer to work along the end-to-end ML lifecycle, alongside ourexisting Product & Engineering team. About Trudenty: TheTrudenty Trust Network provides personalised consumer fraud riskintelligence for fraud prevention across the commerce and paymentsecosystem, starting with first-party and APP fraud prevention. Weare at an exciting point in our journey, as we go to market anddrive growth of The Trudenty Trust Network. This next chapter ofour story is one in which we will drive impact across the commerceecosystem, create to stay at the leading edge of innovation acrossthe industry whilst building material value for our team (inclusiveof shareholders). We are a 10 person seed stage company that hassecured partnerships with notable names in the payments andcommerce ecosystem, and raised investment from our first choice ofpartners who align with our values and ambition for the future. Ourteam is one of exceptional ‘outliers’; defined by grit, resilience,creativity in problem solving, intelligence and mastery of ourdomains. We are also mission-driven and results-oriented. Workingwith us, you will get the opportunity to do some of the best workof your life and unfold your full potential as a human. We are aremote team, that co-works from London frequently. So easy travelinto London should be possible for everyone in our team. The roleWe are looking for a Machine Learning Engineer with a spike in dataengineering and maintaining real-time data pipelines. You will workwith our Product & Engineering team along the end-to-endalgorithm lifecycle to advance the Trudenty Trust Network. A bitmore on what you’ll do: Data Engineering 1. Develop and maintainreal-time data pipelines for processing large-scale data 2. Ensuredata quality and integrity in all stages of the data lifecycle 3.Develop and maintain ETL processes for data ingestion andprocessing Algorithm Development, Model Training and Optimisation1. Design, develop, and implement advanced machine learningalgorithms for fraud prevention and user personalization 2. Trainand fine-tune machine learning models using relevant datasets toachieve optimal performance 3. Implement strategies for continuousmodel improvement and optimization Data Mining & Analysis 1.Apply data mining techniques such as clustering, classification,regression, and anomaly detection to discover patterns and trendsin large datasets. 2. Analyze and preprocess large datasets toextract meaningful insights and features for model training MLOps -Deployment into production environments, Monitoring and Maintenance1. Experience deploying and maintaining large-scale ML inferencepipelines into production 2. Implement and monitor modelperformance in production environments on Kubernetes and AWS cloudplatforms. 3. Utilize Docker for containerization and orchestratecontainerized applications using Kubernetes. Code Review andDocumentation 1. Conduct code reviews to ensure high-quality,scalable, and maintainable code 2. Create comprehensivedocumentation for developed algorithms and models Collaboration 1.Collaborate with our cross-functional team; including the founders,sales, data scientists, engineers, and product to understandbusiness requirements and implement effective solutions Researchand Innovation 1. Stay abreast of the latest advancements in fraudprevention and machine learning and contribute to the explorationand integration of innovative techniques About you: You will haveproven experience with data science and a track record ofimplementing fraud prevention, credit scoring or personalizationalgorithms. Setting up and maintaining real data pipelines to feedyour ML models is light work for you, and you would have been ascomfortable if this JD was for a ‘data engineer’. You have workedin a high growth and fast moving company. You are agile,comfortable with ambiguity and are a creative thinker who can applyresearch and past experiences to new problems. What we’re lookingfor: 1. Education & Experience: 1. Bachelor's or Master'sdegree in Computer Science, Data Science, or a related field. 2. 5+years of professional experience in a relevant area like fraudprevention or credit scoring 2. Machine Learning Expertise: 1.Strong understanding of machine learning algorithms and theirpractical applications, particularly in fraud prevention and userpersonalization. 2. Experience designing, developing, andimplementing advanced machine learning models. 3. Familiarity withmachine learning frameworks such as TensorFlow, PyTorch, andscikit-learn. 3. Data Engineering Skills: 1. Proficiency indeveloping and maintaining real-time data pipelines for processinglarge-scale data. 2. Experience with ETL processes for dataingestion and processing. 3. Proficiency in Python and SQL. 4.Experience with big data technologies like Apache Hadoop and ApacheSpark. 5. Familiarity with real-time data processing frameworkssuch as Apache Kafka or Flink. 4. MLOps & Deployment: 1.Experience deploying and maintaining large-scale ML inferencepipelines into production environments. 2. Proficiency with Dockerfor containerization and Kubernetes for orchestration. 3.Familiarity with AWS cloud platform (experience with GCP or Azureis a plus). 4. Experience monitoring and optimizing modelperformance in production settings. 5. Programming Languages: 1.Strong coding skills in Python and SQL. 2. Experience with Node.js,JavaScript (JS), and TypeScript (TS) is a plus. 6. StatisticalKnowledge: 1. Solid understanding of statistical concepts andmethodologies for analyzing and interpreting large datasets. 2.Ability to apply statistical techniques to validate models andalgorithms. 7. Data Manipulation & Analysis: 1. Proficient indata manipulation and analysis using tools like Pandas, NumPy, andJupyter Notebooks. 2. Experience with data visualization tools suchas Matplotlib, Seaborn, or Tableau to communicate insightseffectively. Our offer: 1. Cash: Depends on experience 2. Equity:Generous equity package, on a standard vesting schedule 3. Impact& Exposure: Work at the leading edge of innovation building ourmachine-learning powered smart contracts for fraud prevention 4.Growth: An opportunity to wear many hats, and grow into a role youcan inform 5. Hybrid work: Flexibility to work from home, withtravel into London The process: 1. Submit your CV along withanswers to the handful of questions we ask of every candidate 2. A60min call to explore initial fit with the founders 3. A 60mintechnical problem solving interview, alongside your potential MLcolleague 4. Final discussion with the Founder CEO to align beforewe make a formal offer #J-18808-Ljbffr