The Role
Build and deploy classical machine learning and generative AI solutions, including regression, classification, clustering, LLM, RAG, and prompt-engineering applications. Develop real-time APIs, integrate vector databases, optimize inference, monitor production models, and promote safe, explainable, unbiased AI outputs. Collaborate with business teams to translate requirements into ML use cases. Required expertise includes Python, common data science libraries, LLM frameworks, transformer architecture, NLP, REST APIs, cloud platforms, Docker, and Kubernetes.
Summary Generated by Built In
Job Summary
We are looking for an experienced AI & Generative AI Developer who can work across the AI spectrum—from classical machine learning models to cutting-edge Generative AI applications. The role demands strong experience in building ML models using regression, classification, and tree-based algorithms, along with hands-on exposure to LLMs and generative frameworks like GPT, Stable Diffusion, and LangChain.
Key Responsibilities
🔹 Classical AI/ML
- Design and implement supervised and unsupervised ML models including:
- Linear Regression, Logistic Regression
- Decision Trees, Random Forest, XGBoost
- Naive Bayes, K-Means, SVM, PCA, etc.
- Preprocess and analyse structured/tabular datasets
- Evaluate models using metrics like accuracy, precision, recall, ROC-AUC, and RMSE
- Build predictive models, deploy them into production, and monitor performance
- Collaborate with business teams to translate requirements into ML use cases
🔹 Generative AI (GenAI)
- Build and fine-tune LLMs (e.g., GPT, LLaMA, PaLM) for summarisation, Q&A, document generation, etc.
- Implement prompt engineering, RAG pipelines, and vector database integrations
- Use libraries like Hugging Face Transformers, LangChain, and LlamaIndex
- Develop APIs to expose GenAI models in real-time apps
- Optimise model inference using quantisation, batching, etc.
- Ensure safe, explainable, and bias-free output in alignment with AI ethics guidelines
Required Skills & Qualifications
- Bachelor’s or Master’s in Computer Science, Data Science, Statistics, or related field
- Strong programming skills in Python, with experience in NumPy, Pandas, Scikit-learn
- Proficiency in classical ML algorithms (regression, trees, naive Bayes, etc.)
- Experience with LLM frameworks like OpenAI API, Hugging Face, and LangChain
- Understanding of transformer architecture, NLP, embeddings, and tokenisation
- Familiarity with REST API development using FastAPI/Flask
- Exposure to cloud platforms (AWS/GCP/Azure) and Docker/Kubernetes
Preferred / Nice to Have
- Experience with deep learning (TensorFlow, PyTorch)
- Exposure to image/audio/video generation using models like DALL·E, Stable Diffusion, Whisper
- Familiarity with RAG, LLMOps, and vector stores (FAISS, Pinecone, Weaviate)
- Knowledge of MLOps pipelines, model monitoring, and CI/CD for ML
Skills Required
- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related field
- Strong programming skills in Python
- Experience with NumPy, Pandas, and Scikit-learn
- Proficiency in classical machine learning algorithms, including regression, decision trees, naive Bayes, and related methods
- Experience with OpenAI API, Hugging Face, and LangChain
- Understanding of transformer architecture, NLP, embeddings, and tokenization
- Familiarity with REST API development using FastAPI or Flask
- Exposure to AWS, GCP, or Azure
- Exposure to Docker and Kubernetes
- Experience with TensorFlow or PyTorch
- Experience with image, audio, or video generation using DALL-E, Stable Diffusion, or Whisper
- Familiarity with RAG, LLMOps, and vector stores such as FAISS, Pinecone, or Weaviate
- Knowledge of MLOps pipelines, model monitoring, and CI/CD for machine learning
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The Company
What We Do
FactEntry is a London-headquartered provider of fixed-income reference data, bond pricing, analytics, and related data solutions for debt-capital-markets professionals. Its products include a validated securities master and reference database, bond valuation engine, corporate-actions tracking, bond-document database, municipal disclosures, and regulatory data services. The company combines analyst-led research with machine learning and natural-language-processing technologies to deliver reliable market data and monitoring.
