Overview

End Date Thursday 22 October 2026

Responsibilities

• Solution Delivery
• Team & Stakeholder Engagement
• Manage and mentor a team of data scientists, data engineers, and ML engineers. Ability to influence business, technology, risk, and architecture stakeholders while leading multidisciplinary teams and driving enterprise AI adoption Influence architectural decisions through clear communication and stakeholder alignment. Drive adoption of architectural standards and reusable assets across delivery teams.

Qualifications

• experience in AI/ML solution architecture across multiple use cases and platforms. Proven experience operationalising AI solutions from PoC to production, including production readiness, monitoring, support models, scalability, and value realisation Deep hands‑on experience designing and implementing GenAI, RAG, Agentic AI, LLM-based solutions, and enterprise AI platforms at scale Strong
• understanding of cloud platforms, preferably Google Cloud Platform (GCP) and Microsoft Azure. Strong foundation instatistics, machine learning, anddeep learning. Proven
• experience in building and deployingGenAI solutionsusing frameworks likeLangChain,LangGraph, or similar. Expertise inNatural Language Processing (NLP), including semantic search, entity recognition, and text generation. Hands‑on
• experience withLLMs(e.g., GPT, LLaMA, Claude, Mistral) and fine‑tuning/customisation techniques. Expertise in designing and implementing scalable MLOps, LLMOps, CI/CD, model lifecycle management, AI observability, and enterprise AI platform capabilities. Deep
• understanding of AI governance, Responsible AI, model assurance, security, privacy, risk management, and regulatory compliance frameworks Experience implementing AI monitoring, model performance management, drift detection, SLA management and operational reporting. Ability to design and implement scalable, reusable, and secure AI/ML components.
• Knowledge of AI security controls including prompt injection protection, data protection, DLP, privacy and AI guardrails Experience working with cross‑functional teams and influencing architectural decisions. Strong communication and stakeholder management skills.
• A degree in Computer Science, Data Science, AI/ML, or related field. Experience with GenAI, LLMs, and autonomous agent architectures. Prior experience in enterprise‑wide architectural initiatives involving AI/ML. Familiarity in cloud platforms (Azure, AWS, GCP) and containerisation (Docker, Kubernetes). Knowledge of enterprise AI governance, ethical AI, and model interpretability. Certification in TOGAF or equivalent enterprise architecture frameworks is a plus.
• Should you wish to contact us for any reason, please email us at: indiarecruitment@lloydsbanking.com For more Flexible Working Options please use the free text search, e.g. job sharing, variable hours, to identify relevant matches.