About Us

Skybound Wealth Management is a global financial advisory company with employees across the UK, USA, Switzerland, Cyprus, Spain and UAE. We provide tailored financial advice to international clients, supported by expert teams across wealth planning, compliance and operations.

Role Overview

We are seeking an experienced and technically capable AI Platform & LLMOps Engineer to take ownership of the reliability, performance and economics of our models, prompts, agents and AI services in production.

Working closely with our software engineering, data, cybersecurity and product teams, the AI Platform & LLMOps Engineer will ensure our AI services are observable, resilient, repeatable, secure and cost-controlled.

Key Responsibilities

AI Platform Operations

- Operate and maintain the Group’s model gateway and AI provider integrations.
- Manage authentication, model routing, fallback processes, rate limits, quotas and provider controls.
- Maintain controls relating to model and provider versions, deprecations and service changes.- Monitoring
- Build and maintain dashboards and alerts covering token usage, cost, latency, error rates and service availability.
- Monitor quality and safety evaluations, tool failures and AI usage by person, team, product and use case.
- Implement distributed tracing, metrics and logging using OpenTelemetry or equivalent technologies.
- Define and maintain service-level objectives, operational thresholds and escalation processes.
- Identify emerging performance, reliability or cost issues before they affect users or business operations.

Release Management

- Build prompt, agent and model release pipelines with appropriate version control and approval processes.
- Implement automated testing and evaluation as part of the AI release lifecycle.
- Manage canary deployments,



controlled releases and rollback processes.
- Define and measure model accuracy, service quality and business value, rather than focusing solely on infrastructure performance and cost.
- Establish evaluation frameworks that measure whether AI services successfully complete the intended task, case, document or workflow.
- Monitor model performance and identify potential drift or deterioration in output quality.

Cost Management & Optimisation

- Develop transparent unit economics for AI services, progressing from cost per token to cost per successful task, case, document or workflow outcome.
- Monitor and report AI consumption and expenditure across teams, products and use cases.
- Optimise spend through model routing, caching, batching, prompt and context reduction, model selection and capacity planning.
- Balance cost, speed, accuracy and quality requirements when selecting and configuring AI services.
- Explain cost and quality trade-offs clearly to engineering, finance and executive stakeholders.

Resilience & Incident Management

- Conduct load, resilience and red-team testing across AI services and supporting infrastructure.
- Develop and maintain operational playbooks for provider outages, model drift, data leakage, prompt injection and unexpected cost increases.
- Investigate and respond to incidents affecting AI services.
- Maintain incident records,



post-incident reviews and remediation plans.
- Assess provider and model changes to identify operational, security and business risks.

Cloud Infrastructure & DevOps

- Manage AI infrastructure through infrastructure-as-code and automated deployment practices.
- Build and maintain CI/CD pipelines, containers, serverless compute and environment separation.
- Manage secrets, authentication, networking, telemetry and access controls.- Maintain clear technical documentation covering architecture, deployment processes, dependencies and operational procedures.

Security & Governance

- Embed DevSecOps and secure software development lifecycle practices across the AI platform.- Implement automated security testing within CI/CD pipelines.
- Maintain dependency, vulnerability and software supply-chain controls.
- Ensure secrets, credentials and sensitive information are managed securely.
- Support auditability, data protection and regulatory requirements relating to financial and client information.
- Maintain appropriate audit evidence, operational records and provider assessments.

Requirements : Essential

- 5+ years’ experience in platform engineering, SRE, DevOps, MLOps, cloud engineering or a related role, including responsibility for production services and incident response.
- Hands-on experience operating LLM or machine-learning services in production, with a strong understanding of token usage, context limits, latency and probabilistic failure.
- Strong Python or scripting capability, with practical experience of CI/CD, infrastructure-as-code, containers and cloud-native services.
- Experience implementing observability using tools such as OpenTelemetry, including tracing, metrics, logging, dashboards and alerting.- P

📌 Ai Engineer (Mumbai)
🏢 Skybound Wealth Management
📍 Mumbai