Role & responsibilities Software Engineering Excellence Implement version control best practices with clear commit messages, feature branches, and stable main branch management Establish CI/CD pipelines for automated builds, tests, and deployments to enable faster, safer releases Drive adoption of clean code principles (KISS, YAGNI, DRY, SOLID) to reduce complexity and improve maintainability Implement comprehensive logging and monitoring strategies for audit trails and production support DevOps & Infrastructure Management Design and implement multi-environment deployment strategies (Development UAT QA Production) Establish Infrastructure as Code (IaC) practices using tools like Terraform or CloudFormation Create robust testing environments to prevent production issues and enable safe experimentation Implement automated testing frameworks for AI/Automation solutions including unit, integration, and end-to-end testing Cloud & Platform Engineering Lead cloud deployment initiatives on AWS and GCP platforms Design scalable, secure, and cost-effective cloud architectures for AI solutions Implement cloud-native, serverless deployment strategies for flexible scaling and global accessibility Establish monitoring, alerting, and observability practices for production AI systems Solution Architecture & Best Practices Establish design standards and process documentation (PDD) to ensure consistent, organized automation development Implement configuration management practices to eliminate hard-coding and enable business user flexibility Create reusable component libraries and shared workflows to accelerate development and improve maintainability Establish quality assurance processes including testing protocols and output validation procedures Cross-Functional Collaboration Interface with various internal teams to coordinate deployment, environment setup, and integration requirements Translate business requirements into technical specifications and implementation plans Collaborate with security, compliance, and governance teams to ensure solutions meet enterprise standards Provide technical expertise to support business stakeholders and solution adoption Hands-On Technical Contribution Actively contribute to development work, focusing on high-impact improvements to maximize team productivity Troubleshoot complex technical issues and provide architectural guidance Prototype new technologies and evaluate their fit for our solution stack Participate in code reviews and provide technical mentorship Required Qualifications Technical Experience 8+ years of software development experience with strong programming skills in Python, Java, or similar languages 3+ years of engineering management experience leading technical teams 5+ years of cloud platform experience (AWS/GCP) including containerization, orchestration, and serverless technologies 3+ years of DevOps experience including CI/CD, Infrastructure as Code, and automated testing Experience with AI/ML frameworks and tools (TensorFlow, PyTorch, Hugging Face, etc.) Preferred candidate profile
Role & responsibilities Must Have: 2-5 years of professional experience in AI, Machine Learning, Data Science, and/or Automation roles. Hands-on experience with developing and deploying AI solutions, with expert understanding of Generative AI concepts (e.g., LLMs, RAG architecture) and/or Agentic AI principles. Proficiency in Python programming and experience with AI/ML libraries (e.g., LangChain, LlamaIndex, Hugging Face). Familiarity with cloud-based AI/ML services (Azure OpenAI, GCP Vertex AI, or AWS Bedrock) and basic understanding of deployment processes. Basic understanding of data pipelines and MLOps concepts. Experience with prompt engineering and optimization techniques for LLMs. Knowledge of vector databases (e.g., Pinecone, Weaviate, ChromaDB) for semantic search and retrieval. Understanding of AI agent architectures including planning, reasoning, and tool-use capabilities. Strong problem-solving skills and a keen interest in learning new technologies. Good communication and teamwork skills with ability to translate technical concepts to non-technical stakeholders. Good to Have: Experience with specific Generative AI frameworks or libraries (LangGraph, AutoGen, CrewAI, Semantic Kernel). Exposure to building or working with multi-agent systems and agent orchestration patterns. Familiarity with document understanding techniques, such as data extraction, OCR, or intelligent document processing (IDP). Experience with fine-tuning or adapting foundation models for domain-specific tasks. Knowledge of evaluation frameworks and metrics for LLM applications (e.g., RAGAS, human-in-the-loop evaluation). Understanding of enterprise AI governance, responsible AI principles, and security best practices. Experience implementing memory systems and context management for conversational AI. Familiarity with streaming responses and real-time AI applications. Experience using modern AI development tools (e.g., GitHub Copilot, Cursor) to enhance productivity. Knowledge of function calling, tool integration, and API orchestration in agentic systems. Experience with A/B testing and continuous improvement of AI systems. Relevant coursework or certifications in AI/ML (e.g., Azure AI Engineer, AWS ML Specialty, Google Cloud ML Engineer). Exposure to accounting, finance, or ERP systems for domain-specific AI applications.
Role & responsibilities Knowledge & Skills required Good understanding of financial reference and/or market reference data. Data modelling skills and a working knowledge of the difference between conceptual, logical and physical models. In-depth knowledge of database structures for reference data as well as methods of working for sourcing and capturing this data Understanding of UML and ERD for logical data models is essential for this role Understanding of RDBMSs and proficiency with SQL Experience of Sparx Systems Enterprise Architect, or other visual data modelling tools desirable but not essential Understanding of JSON and/or XML essential Knowledge & Skills preferred, but not required Strong analytical skills with methodical and independent approach to problem solving Excellent verbal and written communication skills Eagerness to learn and collaborate with others, quick learner and able to work with little supervision Able to liaise across the organization, including News, Content Ops, Product Management, and Technology Stakeholder Management experience and skills - able to converse with all levels of stakeholders to understand and extract the required business requirements Excellent oral and written communication skills Collaborative approach Organize and facilitate meetings & workshops and record actions and decisions Exposure and understanding of different agile approaches and good working knowledge of analysis tools including Jira Responsibilities Working closely with subject matter experts & data architects, analyze the data requirements & produce the Data Model Identify problems and issues in the current architecture Participate in the generation of the physical implementation of the data model Execute governance related activities, participating in reviews & ensuring conformance to standards Certifications / Education University degree strongly preferred Professional Experience At least 5 years of data modelling experience, preferably within the financial services industry working on large scale data migration programmes Full project lifecycle implementation experience including familiarity of agile projects