AI/ML Solutions - Engineering Lead

End Date
Thursday 22 October 2026

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Flexible Working Options
Hybrid Working

Job Description Summary
Leads the desigining and implementation of solutions leveraging AI and machine learning techniques to solve complex business problems, acting as a point of technical expertise and thought leadership, and provides line management and/or coaching to grow team capability

Job Description

Role Overview

Experience : 15+ years

location : Hyderabad

As Lloyds embarks on a transformative journey to become a purpose-driven, AI-powered organisation, we are seeking an experienced AI/ML Solution Architect to bring architectural clarity and coherence to our AI/ML initiatives across business units and platforms. This role is critical in identifying and designing reusable capabilities, common patterns, and golden paths that unify solution delivery across diverse use cases. You will collaborate closely with stakeholders in the CTO function and Enabling Platform teams to ensure scalable, secure, and efficient AI/ML architectures.

In addition to strategic architectural thinking, the ideal candidate will be hands-on, capable of delivering end-to-end solutions from ideation to deployment, while leading and mentoring a multidisciplinary team.

Key Responsibilities

Architectural Leadership

- Ability to define and govern scalable enterprise AI architectures, standards, reusable patterns, and platform strategies across GenAI, RAG, Agentic AI, and AI platforms.

- Define and evolve the architectural blueprint for AI/ML solutions developed across the AI CoE.

- Identify reusable components, services, and patterns that can be leveraged across platforms and use cases.

- Collaborate with CTO and platform teams to align AI/ML architecture with enterprise technology strategy.

- Establish and promote golden paths for AI/ML development, deployment, and governance.

- Shape the target-state architecture for enterprise AI platforms, AI Control Tower capabilities and federated AI delivery models.

Solution Delivery

- Translate business requirements into robust, scalable AI/ML solutions.

- Proven capability to deliver AI solutions end-to-end from ideation and PoC through production deployment, MLOps/LLMOps, monitoring, and operational support

- Collaborate with cross-functional teams to ensure seamless integration of AI capabilities into products and platforms.

- Lead the design and implementation of AI/ML solutions from data ingestion and transformation to model development, evaluation, and deployment.

- Apply best practices in MLOps, DevOps, and scalable cloud-native architectures.

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.

Required Skills & Experience

- Proven 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 in statistics, machine learning, and deep learning.

- Proven experience in building and deploying GenAI solutions using frameworks like LangChain, LangGraph, or similar.

- Expertise in Natural Language Processing (NLP), including semantic search, entity recognition, and text generation.

- Hands-on experience with LLMs (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.

Preferred Qualifications

- 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.