Senior Director - Cloud & AI Platform Engineering

What are we looking for
real solver?
Solver? Absolutely. But not the usual kind. We're searching for the architects of the audacious & the pioneers of the possible. If you're the type to dismantle assumptions, re-engineer ‘best practices,’ and build solutions that make the future possible NOW, then you're speaking our language.
Your Mission: The Role
solving for better.
You own delivery for Cloud & AI Platform Engineering: cloud foundations and migration, data platforms, AI and MLOps foundations, private AI, and the agent runtime that sits on top of them. Every CAPE engagement you lead ends with something running in production, with a named owner, at a cost the client can defend.
Most firms split this work into a cloud team, a data team, an AI team and a FinOps function on the side. Agents ignore those boundaries, so the failures land in the gaps between them. You run CAPE as one system, and you hold it to one bar: evidence from production, not a plan someone approved.
If you have led platform delivery at enterprise scale, you are fluent in how agents actually fail, and you treat utilisation and inference cost as seriously as architecture, this is your seat.
Your Responsibilities
what you will wake up to solve.
‣Make the estate agent-ready
Own platform delivery end to end: Lead every CAPE engagement across landing zones, migration, data platforms, MLOps and private or sovereign AI, with running production systems as the finish line rather than approved plans.
Build the agent runtime properly: Deliver durable execution, multi-agent orchestration, tool interoperability and agent observability, so client agents survive mid-run failures, act safely across systems, and can be operated by whoever inherits them.
Engineer what models can see: Treat retrieval, context engineering and decision instrumentation as first-class deliverables, judged by the business decisions they improve.
‣Run delivery on evidence, not optimism
Own the delivery economics: Be accountable for billable utilisation, effort efficiency and deviation from SOW across the CAPE portfolio, and make sure we have the people before we promise the work.
Deliver cost as an outcome: Make cloud and AI economics part of the engagement: cost allocation, commitment restructuring and lower cost per AI task without a drop in quality, proven by measurement.
Report performance honestly: Keep partner-funded work visible separately, so margin and utilisation reflect what the business unit actually earns.
‣Nothing ships unowned
Launch with an owner: Every platform goes live with someone accountable for running it and the telemetry they need from day one.
Escalate early: Surface risks to the client-facing lead before the client has to raise them.
Plan the next step at launch: Every go-live comes with a view of what to improve next, so delivery becomes a relationship, not a closing date.
‣Build the bench by route, not by default
Hold the credential bar: Set and enforce the certification and hands-on standard for cloud, data, ML and FinOps crafts, requalified on real delivery evidence, with leaders held to the same bar as engineers.
Choose build or buy per capability: Grow existing strengths through training, and hire where the market is moving faster than training can, such as agent platform engineering.
Turn delivery into differentiation: Maintain working accelerators for landing zones, agent platforms and retrieval, publish on what the team has learned, and represent Searce with our cloud and AI partners.
Welcome to Searce
The ‘process-first’, AI-native modern tech consultancy that's rewriting the rules.
We don’t do traditional.
As an engineering-led consultancy, we are dedicated to relentlessly improving the real business outcomes. Our solvers co-innovate with clients to futurify operations and make processes smarter, faster & better. We build alongside our clients. Not for the vanity metrics. But for the transformation to embed lasting competitive advantage for our clients.
The result? Modern business reinvention, built on math, tech, and clarity of purpose.
Functional Skills
Portfolio Judgment: Reads a portfolio of concurrent engagements and knows which one breaks next, and why, before the dashboard turns red.
Delivery Economics: Translates technical scope into effort, capacity, cost and margin, and knows which trade-off protects all of them.
Capability Strategy: Decides, craft by craft, what to train, what to hire and what to stop offering, based on evidence rather than hope.
Standards that Hold: Sets a quality bar people can be measured against, and holds it with leaders as firmly as with new joiners.
Executive Candour: Delivers bad news early, with options, to client and internal leadership alike.
Tech Superpowers
Cloud Foundations and Migration: Hands-on background in landing zones, networking, identity, infrastructure as code (Terraform), containerisation and large-scale migration on Google Cloud and AWS, at Professional Cloud Architect or Solutions Architect Professional depth.
Data Platform Engineering: Lakehouse, streaming, data quality and lineage on platforms such as BigQuery, Databricks and Kafka.
AI and Agent Runtime: MLOps on Vertex AI or SageMaker, open-weight model serving (vLLM), durable execution (Temporal or equivalent workflow engines), agent orchestration (LangGraph, Google ADK), Model Context Protocol, and agent tracing with OpenTelemetry or Langfuse.
Retrieval and Context: Retrieval architecture, vector and hybrid search, permission-aware retrieval and retrieval-quality evaluation.
Cloud and AI Economics: FinOps practice (allocation, showback, anomaly detection), committed-use and reserved-capacity instruments, and inference cost levers such as model routing, caching and batching.
Experience & Relevance
Experience: 12+ years in cloud, data or platform engineering, including 7+ years leading delivery across multiple client engagements and crafts.
Education: Bachelor's degree in Engineering or Computer Science, or equivalent.
Track Record: Has taken enterprise platforms that run AI or agent workloads into production for multiple clients.
Still Builds: Has personally shipped something with a large language model in the recent past, not only overseen a team that did.
Team Scale: Has led a delivery organisation of engineers and architects across more than one craft.
Join the ‘real solvers’
ready to futurify?
If you are excited by the possibilities of what an AI-native engineering-led, modern tech consultancy can do to futurify businesses, apply here and experience the ‘Art of the possible’.
Don’t Just Send a Resume. Send a Statement.

Work arrangement
No