About the team:
In today’s highly competitive private equity market, firms must not only deliver superior returns, but also respond to more stringent reporting requirements and increasing demands for information – both from within and outside their organization. Throughout the industry there is mounting pressure on organizations to do more, requiring a clear technology strategy that not only addresses the demands of today, but also enables the growth and performance of tomorrow.
What You Will Do:
Infuse AI Across the Engineering Lifecycle
• Identify opportunities to apply AI/ML across:
• code development and review
• testing and quality engineering
• CI/CD pipelines
• production operations and SRE
• incident management and root‑cause analysis
• Prototype, validate, and productionise AI‑assisted workflows, tools, and accelerators that improve:
• developer productivity
• reliability and resilience
• signal‑to‑noise ratio in operations
• speed of diagnosis and recovery
Build Practical AI Systems (Not Slideware)
• Design and implement working AI/ML solutions, not just experiments:
• LLM‑powered developer or ops assistants
• anomaly detection and predictive insights
• intelligent alert triage and noise reduction
• knowledge mining from logs, tickets, runbooks, incidents
• Focus on value, adoption, and usability, not research perfection.
Reimagine How Engineering Works
• Challenge existing processes and ask:
• “Why do humans still do this?”
• “Could AI do this better, faster, or continuously?”
• Partner with engineering, SRE, and platform teams to embed AI into real workflows, not bolt‑ons.
• Act as a change agent, helping teams adopt new ways of working.
Collaborate Broadly, Execute Independently
• Work closely with:
• core MBP engineering teams
• platform and SRE engineers
• security and tooling teams
• Translate vague ideas into concrete PoCs, then into scalable solutions.
• Balance experimentation with engineering discipline.
Stay Ahead of the Curve
• Track advances in:
• LLMs, agents, and retrieval‑augmented systems
• MLOps and AI platforms
• developer‑facing AI tooling
• Apply new ideas selectively—signal over hype.
• Not a banking domain specialist role
• Not a pure data scientist / research role
• Not an ivory‑tower “AI strategy” role
This role is about making engineering better—now.
What you Bring:
• 4–6 years of experience in software engineering, AI/ML engineering, or applied ML roles
• Strong programming skills in at least one mainstream language (e.g. Python, Java, Go, Scala)
• Hands‑on experience with:
• ML or AI systems in production OR
• building intelligent tooling using LLMs / rules + ML combinations
• Solid understanding of:
• software engineering fundamentals
• distributed systems basics
• CI/CD and modern development practices
• Proven ability to work in ambiguous problem spaces and self‑direct work.
What we offer you:
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Sourcing Model
Recruitment at FIS works primarily on a direct sourcing model; a relatively small portion of our hiring is through recruitment agencies. FIS does not accept resumes from recruitment agencies which are not on the preferred supplier list and is not responsible for any related fees for resumes submitted to job postings, our employees, or any other part of our company.
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