Senior Machine Learning Engineer

Zaizi

Senior Machine Learning Engineer deploying quantized SLMs and knowledge graphs on constrained edge hardware. Ensuring dependable, explainable AI for UK defence and intelligence users at Zaizi.

Posted 8/4/2026full-timeLondon • 🇬🇧 United KingdomSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in deploying edge-native AI models and knowledge analytics engines, with a strong focus on model quantization, human-in-the-loop decision support, and the integration of AI safety measures. Proficient in Python and PyTorch, with a solid understanding of NLP and graph-based data structures.

Highest-signal resume keywords

Active UK SC ClearanceModel Quantization TechniquesPython and PyTorch ProficiencyProduction Experience in ML Model DeploymentUnderstanding of AI Safety and Operational Constraints

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Model Quantization TechniquesProduction Experience in ML Model DeploymentNLP and Semantic SummarisationGraph-Based Data StructuresExecution Acceleration Across NPU/GPU HardwareLightweight On-Device Graph DatabasesRelationship Extraction PipelinesContainerised Deployment PipelinesStreaming AnalyticsAnti-Hallucination Controls

Soft Skills

Technical CommunicationStakeholder Engagement

Tools & Technologies

PythonPyTorchHuggingFaceProtobufJSONXMLGraph DatabasesVector EmbeddingsChaquopyJNI

Certifications & Qualifications

Active UK SC Clearance

Industry Keywords

Edge-Native AI ModelsKnowledge Analytics EnginesHuman-in-the-Loop Decision SupportMilitary Sensor FeedsSignals Intelligence (SIGRF)Cursor-on-Target DataDSTLDAICDefence Innovation Programs

Tech Stack

Tools & technologies

AndroidPythonPyTorch

About the role

Key responsibilities & impact

Lead the design, quantization, and deployment of edge-native AI models and knowledge analytics engines
Transition Small Language Models and knowledge graph pipelines into air-gapped, degraded, and bandwidth-constrained tactical hardware
Deploy deterministic, explainable, human-in-the-loop decision-support tools for intelligence and operational users
Quantize, fine-tune, and optimize open-source SLMs and vision-language models for low-power edge runtimes
Design and maintain lightweight on-device graph databases and relationship extraction pipelines for structured and unstructured sensor data
Implement bounding guardrails, prompt evaluation, and anti-hallucination controls
Build reproducible model training, evaluation, and containerised deployment pipelines for air-gapped or low-bandwidth environments
Translate complex ML/AI concepts into technical recommendations for MoD stakeholders, DSTL assessors, and Prime contractors

Requirements

What you’ll need

Active UK SC Clearance (minimum)
Strong understanding of AI safety, non-repudiation, and human-in-the-loop operational constraints
3+ years of production experience deploying ML models to edge runtime environments
Experience with model quantization techniques including INT8, INT4, and AWQ
Experience with execution acceleration across NPU/GPU hardware
Proficiency in Python and PyTorch/HuggingFace ecosystems
Solid foundation in NLP, semantic summarisation, and graph-based data structures
Experience with graph databases, vector embeddings, and network analysis
Understanding of Protobuf, JSON, XML, and streaming analytics
Right to work in the UK without sponsorship
Lived in the UK continuously for the last 5+ years
Desirable: ML runtime integration into Android ART via Chaquopy, JNI, or native C++ libraries
Desirable: military sensor feeds, signals intelligence (SIGRF), or Cursor-on-Target data
Desirable: publications or project delivery with DSTL, DAIC, or Defence Innovation programs

Benefits

Comp & perks

Salaries reviewed annually to ensure they reflect your performance and market value
Loyalty Pension: starting at a 5% employer contribution, increasing by 0.5% every year after the third anniversary, up to a maximum of 8%
Comprehensive Group Life Assurance
10 paid days for Reservist Military Service
25 days annual leave + Bank Holidays
Flexibility to Buy/Sell additional days
2 paid volunteering days per year
Fully funded professional certifications (AWS, GCP, Agile, etc.)
5 days paid study leave
£500 annual "Personal Choice" fund
Access to 1-2-1 professional coaching and team training
Vitality Private Medical Insurance, including Apple Watch, gym discounts, and rewards
Genuine hybrid working
WFH equipment allowance
Cycle to Work scheme
Commitment to sustainable, healthy working practices
Interview-process adjustments and accommodations for candidates with disabilities or who are neurodiverse