Description

AI Automation Engineer | Hybrid 3 days a week in office | London | Permanent

A leading financial services client in London is seeking a talented AI Automation Engineer to join their team. Please see below for key details.

Role Overview: Analyse and optimise business processes for automation whilst designing, building, and deploying intelligent automation solutions using BPA platforms (Appian), Machine Learning, and Generative AI to drive operational efficiency and innovation.

Key Characteristics:

1. Process Analysis & Optimisation - Expert in analysing existing business processes through stakeholder interviews, process mapping, and workflow documentation to identify automation opportunities. Skilled in creating process flow diagrams, conducting time-motion studies, identifying bottlenecks and inefficiencies, and redesigning processes to be machine-readable and automation-ready using methodologies.
2. Python Development - Strong proficiency in Python programming including object-oriented design, asynchronous programming, error handling, and writing clean, maintainable code. Experience with key libraries including Pandas, NumPy for data manipulation, requests and APIs for integrations, asyncio for concurrent processing, and building robust automation scripts with proper logging, testing (pytest), and documentation.
3. AI & Machine Learning Frameworks - Deep expertise in AI/ML frameworks including TensorFlow, PyTorch, Scikit-learn, and Hugging Face Transformers. Experience building, training, and deploying machine learning models for classification, regression, clustering, and NLP tasks. Understanding of model evaluation metrics, hyperparameter tuning, feature engineering, and MLOps practices for production deployment.
4. Generative AI & LLM Integration - Proficient in working with Large Language Models including OpenAI GPT models, Anthropic Claude, Azure OpenAI, and open-source alternatives (Llama, Mistral). Experience with prompt engineering, fine-tuning, RAG (Retrieval Augmented Generation) architectures, vector databases (Pinecone, ChromaDB, FAISS), embeddings, and building AI-powered automation solutions that leverage natural language understanding.
5. Appian BPA Platform - Strong experience with Appian low-code platform including process modelling, interface design, expression rules, integration objects, and data modelling. Skilled in building end-to-end business process applications, configuring workflows, implementing business rules, managing records, and integrating Appian with external systems via REST APIs, web services, and connected systems.
6. API Development & Integration - Proficient in designing and building RESTful APIs using FastAPI, Flask, or Django REST Framework for exposing AI models and automation services. Experience with API authentication (OAuth, JWT), rate limiting, error handling, API documentation (Swagger/OpenAPI), webhooks, and integrating disparate systems to create seamless automated workflows.
7. Document Processing & OCR - Experience implementing intelligent document processing solutions using OCR technologies (Tesseract, Azure AI Document Intelligence, natural language processing for information extraction, document classification, and building end-to-end pipelines for automated document ingestion, processing, and data extraction with validation rules.
8. Robotic Process Automation (RPA) - Knowledge of RPA concepts and tools (UiPath, Automation Anywhere, Power Automate) for automating repetitive tasks, screen scraping, and legacy system integration. Ability to assess when RPA vs. API integration vs. AI solutions are most appropriate, and experience building hybrid automation solutions combining multiple technologies.
9. Data Engineering & Pipeline Development - Strong skills in building data pipelines for AI/automation solutions including data extraction, transformation, and loading (ETL). Experience with SQL databases (SQL Server), data validation, cleansing workflows, scheduling tools (Azure Data Factory), and ensuring data quality for machine learning applications.
10. Machine Learning Operations (MLOps) - Experience deploying ML models to production environments using containerisation (Docker), orchestration (Kubernetes), model versioning (MLflow, DVC), monitoring model performance and drift, A/B testing frameworks, and implementing CI/CD pipelines for automated model training and deployment. Understanding of model governance, explainability, and compliance requirements.
11. Solution Architecture & Technical Design - Ability to design end-to-end automation architectures that combine multiple technologies (BPA, ML, GenAI, APIs) into cohesive solutions. Experience creating technical design documents, system architecture diagrams, assessing build vs. buy decisions, estimating effort and complexity, and presenting technical recommendations to both technical and non-technical stakeholders.
12. Stakeholder Collaboration & Change Management - Excellent communication skills for gathering requirements from business users, translating business needs into technical specifications, and demonstrating proof-of-concepts. Experience managing stakeholder expectations, conducting user acceptance testing, providing training on automated solutions, measuring automation ROI through KPIs (time saved, error reduction, cost savings), and driving adoption of intelligent automation across the organisation.

If you align to the key requirements then please apply with an updated CV.