Python Work, Jobs in Blyth, Nbl (with Salaries)

Senior Machine Learning Engineer
Sage —Newcastle upon Tyne
- Hybrid work
- AWS
- Machine learning
- APIs
Security Firmware/FPGA Engineer
Leonardo UK Ltd —Newcastle upon Tyne
- £37,400 - £84,995 a year
- Full-time
- Hybrid work
- Scripting
- Python
- Flexible schedule
- Referral programme
Full Stack Developer
Version 1 —Newcastle upon Tyne
- Full-time
- AWS
- APIs
- Agile
- Employee assistance programme
- Employee discount
Quick apply
Principal Water Quality Scientist/ Modeller
AECOM —Newcastle upon Tyne
- Full-time
- Hybrid work
- Master's degree
- Project management
- Scripting
Senior AI Product & Research Engineer (Consultant)
Version 1 —Newcastle upon Tyne
- Full-time
- AWS
- AI
- Python
- Employee assistance programme
- Employee discount
Quick apply
8d
Senior Network Engineer
Clifford Chance —Newcastle upon Tyne
- Weekend availability
- Hybrid work
- Azure
- Microsoft Outlook
- Mentoring
Quick apply
Senior Fullstack Developer
Version 1 —Newcastle upon Tyne
- Full-time
- AWS
- JavaScript
- APIs
- Employee assistance programme
- Employee discount
Quick apply
Senior DevOps Engineer - Newcastle
Accenture —Newcastle upon Tyne
- Full-time
- DevOps
- AWS
- Scripting
Senior Software Engineer
OpenWorks Engineering —Prudhoe
- Full-time
- Computer Science
- DevOps
- Engineering
Quick apply
10h

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Senior Machine Learning Engineer

Sage -
Newcastle upon Tyne

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Job details

Qualifications

Law
AWS
Docker
Machine learning
REST
APIs
Python

Full job description

Job Description

We are looking for a Senior ML Engineer to take technical ownership of our machine learning production environment. You will lead the transition of experimental models into production-grade services that are reliable, scalable, and cost-effective. Your mission is to build the "highway" that allows our data science team to deploy models rapidly while ensuring those models are observable and fiscally responsible. You will own the entire ML lifecycle—from automated training pipelines to real-time inference clusters—and serve as a key software engineering contributor to our AI product stack.

This is a hybrid role – three days per week in our Newcastle office.

Key Responsibilities

In this role your key responsibilities will be:

Lifecycle & Pipeline Architecture: Design and own the automated "Continuous Training" (CT) and deployment pipelines. Architect reusable, modular infrastructure for model training and serving, ensuring the entire lifecycle is versioned and reproducible.

Software Engineering Best Practices: Lead the team in adopting professional engineering standards. This includes owning the strategy for unit/integration testing, peer code reviews, and applying SOLID principles to ML codebases to ensure they remain modular and maintainable.

ML Observability: Establish and own the telemetry framework for the AI stack. Implement proactive monitoring for system health and model-specific metrics, such as data drift, concept drift, and prediction accuracy.

FinOps & Cost Management: Own the strategy for AI cloud spend. Build monitoring and alerting frameworks to track compute costs (training and inference) and implement optimization strategies like auto-scaling and spot-instance usage.

AI Systems Engineering: Act as a lead software engineer to integrate models into the product ecosystem. Develop high-performance, secure APIs and microservices that wrap our ML capabilities for production consumption.

Data & Model Governance: Own the versioning strategy for the "Holy Trinity" of ML: code, data, and model artifacts. Ensure clear documentation and audit trails for all production deployments.

What we're looking for:

Essential skills (entry requirements):

Demonstrating strong software engineering fundamentals, including production‑quality Python, testing, CI/CD practices, and version control

Designing and operating reliable, versioned REST APIs using an API‑first approach

Building, deploying, and operating backend services in cloud environments, with AWS as the primary platform (experience on other major clouds considered transferable)

Using containerisation and modern deployment approaches, including Docker, automated pipelines, and basic observability

Working effectively with real‑world data and production systems in collaboration with product, data, and platform teams

Bringing either hands‑on experience delivering machine‑learning systems in production or a very strong software‑engineering background with clear motivation to grow into ML and MLOps

Desirable skills (strong differentiators):

Using AWS SageMaker for training, deploying, and operating machine‑learning workloads, or demonstrating equivalent experience on similar cloud ML platforms

Exposing machine‑learning models via APIs (e.g. FastAPI‑based inference services) and operating them reliably at scale

Applying MLOps practices, including model and version management, monitoring, and handling model or data drift

Implementing advanced service patterns such as asynchronous processing, event‑driven architectures, or multi‑version services

Serving LLM or GenAI‑based capabilities in production, including model serving, RAG pipelines, and inference controls

Designing reusable, platform‑level services and shared ML patterns rather than one‑off implementations

Managing cloud operational trade‑offs, including cost efficiency, latency, scalability, and reliability

#LI-MD1

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python jobs in blyth, nbl

Senior Machine Learning Engineer

Security Firmware/FPGA Engineer

Full Stack Developer

Principal Water Quality Scientist/ Modeller

Senior AI Product & Research Engineer (Consultant)

Senior Network Engineer

Senior Fullstack Developer

Senior DevOps Engineer - Newcastle

Senior Software Engineer

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