The Senior AI/ML Engineer – MBP is a high‑impact individual contributor who acts as a force multiplier across the SDLC, development, operations, and SRE by applying AI and ML in creative, pragmatic, and scalable ways.

This role exists to reimagine how engineering teams build, test, deploy, operate, and support MBP, using AI as a lever—not to “add AI features”, but to change how work gets done.

The ideal candidate is:

• an innovator, not a rule‑follower,

• comfortable operating in ambiguity,

• and excited to use AI to challenge existing engineering assumptions.

Deep banking domain knowledge is not required. Curiosity, creativity, and strong engineering fundamentals are.

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.

Preferred (but Not Mandatory)

• Experience applying AI to:

   • developer productivity

   • observability or incident analysis

   • automation or operational tooling

• Familiarity with:

   • LLMs, prompts, agents, embeddings

   • MLOps concepts

   • cloud platforms and containerised environments

• Prior exposure to platform, infra, or SRE teams.

What Success Looks Like

• Engineering and SRE teams noticeably work faster and smarter because of what you built.

• Repetitive, manual, or low‑value tasks are replaced or augmented by AI‑driven workflows.

• AI solutions are used, not shelved.

• MBP engineering begins to think differently about how work gets done.

Why This Role Is Special

• You will influence how hundreds of engineers operate, not just one product feature.

• You will have freedom to experiment, fail fast, and re‑try.

• Your impact is measured in leverage, not lines of code.

Privacy Statement

FIS is committed to protecting the privacy and security of all personal information that we process in order to provide services to our clients. For specific information on how FIS protects personal information online, please see the Online Privacy Notice.

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.

#pridepass