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Senior or Staff AI & ML Engineer
Generative AI Infrastructure at Scale – UK-Based
Experience the core of applyogenic AI development—building the production-grade systems that power large language models under real-world constraints.
Our client, a leader in application security and traffic management, is expanding its AI Core group and is seeking a hands-on Senior or Staff AI & ML Engineer. If you thrive on ownership over a serving stack (not just notebooks) and measure success by what holds up under production rather than demos, this is the role for you.
What You Will Own
This is end-to-end serving architecture—not pilot projects. You’ll own the delivery pipeline from model selection to real-time inference, setting the technical standards and leading a team of engineers to ship features customers depend on.
Technical Responsibilities
- Build and scale generative AI features from initial modelling (fine-tuning via LoRA/PEFT) through to TTFB-optimised inference infrastructure
- Design multi-agent and retrieval-augmented generation (RAG) systems, ensuring scalability, cost-efficiency, and observability
- Anomaly detection and performance monitoring at scale
- NVIDIA Triton & TensorRT optimisation: Advanced ISP tuning for low latency, high throughput and GPU utilisation
- Python/FastAPI microservices: Reliable, containerised, and orchestrated with Kubernetes fidelity
- Technical leadership: Influence architecture, go-to-person for quality bar debates, mentor engineers
- Bridge between research & product: Fast-track experimental AI into production systems
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Requirements
Core Skills & Experience (Non-Negotiable)
- 5–10 years of progressive ML/AI engineering, with track record of leading shipping/research/implementation at a senior level
- Production-grade inference experience:
- NVIDIA Triton for serving multi-model pipelines
- TensorRT optimisations ( spost-assembly, kernel fusion, performance profiling)
- GenAI depth with hands-on experience in:
- Fine-tuning methods (LoRA, PEFT, GPTQ)
- LLM + RAG toolchains
- Agentic systems and workflow orchestration
- Python & FastAPI microservices initially, then orchestrated on Docker & Kubernetes across a cloud provider (GCP/AWS/Azure)
- Drive with latency-sensitive, low-cost, high-throughput requirements
Preferences & Experiments
(Foregrounds intriguing possibilities but is not a bar to apply)
- Beyond Triton: vLLM, FastTransformer, and similar lightweight serving frameworks
- Background in security, networking, or broader trustworthy AI
- MLOps experiences (e.g., data SIGs with Big Data/Snowflake, monitoring, optimisation tools)
- Patterns for service firewalls or token-level filtering (since our day job is traffic management)
Lily’s Guardrails
(Yes—must have access)
- Right to work in the UK
Why Join Us?
The Why
- This isn’t an R&D cough—they build to ship, every day.
- Your product is the bloodline infrastructure of front-end generative AI features.
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The Operator
- Fully remote within the UK—bring proven delivery, not a commute report.
- Phrases like ‘pay as you grow’ will be non-negotiable but not regressive. Paths for staff → principal/principal++ (sponsor/mentorship weighed heavily).
- C-Serv as delivery house: Hire gold-standard candidates only, invest in headcount before output deadlines, and when you’re promoted, we give up the self-ticker to check what money the culture is actually spent on.
The Values
- A female owner founded this: integrity, empowerment, collaboration.
- We ask “will this grow people?” before we ask “will we sell it?”.
- Your focus matters more than the signal-to-noise for contingent deadlines. Yeah, we might have too many meetings… sometimes. (Avoids the need for 80ms shrugs.)
The Gig
- Salary + benefits match the recognised lead impact in production-grade teams.
- Annual, tailor-made skill cape monies to do bone-deep learning.
- Polygon of support: You asked C-Serv preference during hiring; they’ve signed up to fulfil the terms of the [£X Y really?? rigouristng a job] facing hours-by-body metrics train—we let people be.
How Export C-Serv?
- Email your CV, repo links, or any ** poems/bit about *why serverless came as a relief after diskRead errors *** to ai-ml-engineers.uk@c-serv.com
- Consider the ‘Why Your Breadcrumbs Herald Momentum’ challenge: While we like you, the content in the stack you published needs quality: readItemId(buildBot.logBalance()) (thank you for your curiosity).
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