Axiom Recruit | Senior Python Engineer - API Development - Blockchain - UK / EU - Remote

Axiom Recruit
Manchester
2 months ago
Applications closed

Related Jobs

View all jobs

Senior Python Engineer - API


Axiom are hiring for a standout platform to the Blokchain markets, seeking to bring on a talented Python Engineer to lead the API development across their products.


This key role will play a pivotal role in shaping the overall feel and user experience of this exciting and innovative platform.


Fully remote role, based in EU or UK.



Duties & Responsibilities

Working as a lead within the tech team, you will drive development and architectural decisions across a range of blockchain-oriented SaaS platforms.

  • Guide Python projects, API design, and big data solutions.
  • Create scalable Python apps and enhance existing systems with modern tech.
  • Collaborate with teams to deliver practical, high-impact solutions.
  • Support junior engineers and ensure clean, quality code.
  • Prepare technical docs and uphold strong architectural practices.



Requirements:

  • 8+ years in Python development, including API design, optimization, and digital transformation projects.
  • Proficient in PostgreSQL, data warehouses (e.g., Clickhouse, Snowflake), and deploying complex apps on cloud platforms like Kubernetes.
  • Skilled in tackling challenges like job scheduling, concurrency, and scaling while optimizing performance, compute, and memory.
  • Excellent interpersonal skills for working with cross-functional teams and stakeholders.
  • Deep understanding of design patterns, architectural styles, and building robust, scalable technical foundations.



Would be a plus:

  • Skilled in building and maintaining large-scale or real-time data pipelines using tools like Kafka, Hadoop, Hive, and Storm.
  • Background in compliance within finance or crypto, with knowledge of blockchain technologies.
  • Familiar with GoLang, Rust, Terraform, GraphQL, and analytics tools for data visualization and decision-making.
  • Contributed to open-source projects, StackOverflow, and experience with incident management.



The Reward:

  • Fully remote work environment with flexible hours across Europe.
  • Opportunity to work on cutting-edge technology in the blockchain, AI and data intelligence space
  • Competitive salary and bonus schemes
  • Be part of a dynamic, innovative team that values collaboration and continuous learning.

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Job-Hunting During Economic Uncertainty: Machine Learning Edition

Machine learning (ML) has firmly established itself as a crucial part of modern technology, powering everything from personalised recommendations and fraud detection to advanced robotics and predictive maintenance. Both start-ups and multinational corporations depend on machine learning engineers and data experts to gain a competitive edge via data-driven insights and automation. However, even this high-demand sector can experience a downturn when broader economic forces—such as global recessions, wavering investor confidence, or unforeseen financial events—lead to more selective hiring, stricter budgets, and lengthier recruitment cycles. For ML professionals, the result can be fewer available positions, more rivals applying for each role, or narrower project scopes. Nevertheless, the paradox is that organisations still require skilled ML practitioners to optimise operations, explore new revenue channels, and cope with fast-changing market conditions. This guide aims to help you adjust your job-hunting tactics to these challenges, so you can still secure a fulfilling position despite uncertain economic headwinds. We will cover: How market volatility influences machine learning recruitment and your subsequent steps. Effective strategies to distinguish yourself when the field becomes more discerning. Ways to showcase your technical and interpersonal skills with tangible business impact. Methods for maintaining morale and momentum throughout potentially protracted hiring processes. How www.machinelearningjobs.co.uk can direct you towards the right opportunities in machine learning. By sharpening your professional profile, aligning your abilities with in-demand areas, and engaging with a focused ML community, you can position yourself for success—even in challenging financial conditions.

How to Achieve Work-Life Balance in Machine Learning Jobs: Realistic Strategies and Mental Health Tips

Machine Learning (ML) has become a cornerstone of modern innovation, powering everything from personalised recommendation engines and chatbots to autonomous vehicles and advanced data analytics. With numerous industries integrating ML into their core operations, the demand for skilled professionals—such as ML engineers, research scientists, and data strategists—continues to surge. High salaries, cutting-edge projects, and rapid professional growth attract talent in droves, creating a vibrant yet intensely competitive sector. But the dynamism of this field can cut both ways. Along with fulfilling opportunities comes the pressure of tight deadlines, complex problem-solving, continuous learning curves, and high-stakes project deliverables. It’s a setting where many professionals ask themselves, “Is true work-life balance even possible?” When new algorithms emerge daily and stakeholder expectations soar, the line between healthy dedication and perpetual overwork can become alarmingly thin. This comprehensive guide aims to shed light on how to achieve a healthy work-life balance in Machine Learning roles. We’ll discuss the distinctive pressures ML professionals face, realistic approaches to managing workloads, strategies for safeguarding mental health, and how boundary-setting can be the difference between sustained career growth and burnout. Whether you’re just getting started or have been at the forefront of ML for years, these insights will empower you to excel without sacrificing your well-being.

Transitioning from Academia to the Machine Learning Industry: How PhDs and Researchers Can Thrive in Commercial ML Settings

Machine learning (ML) has rapidly evolved from an academic discipline into a cornerstone of commercial innovation. From personalising online content to accelerating drug discovery, machine learning technologies permeate nearly every sector, creating exciting career avenues for talented researchers. If you’re a PhD or academic scientist thinking about leaping into this dynamic field, you’re not alone. Companies are eager to recruit professionals with a strong foundation in algorithms, statistical methods, and domain-specific knowledge to build the intelligent products of tomorrow. This article explores the essential steps academics can take to transition into industry roles in machine learning. We’ll discuss the differences between academic and commercial research, the skill sets most in demand, and how to optimise your CV and interview strategy. You’ll also find tips on networking, developing a commercial mindset, and navigating common challenges as you pivot your career from the halls of academia to the ML-driven tech sector.