Machine Learning Engineer to Cloud Engineer
Step-by-step guide to changing career from Machine Learning Engineer to Cloud Engineer — transferable skills, skill gaps, salary comparison, timeline, and practical advice for the UK market.
Can you go from Machine Learning Engineer to Cloud Engineer?
Moving from Machine Learning Engineer to Cloud Engineer is a realistic career change that many professionals make successfully. Both roles sit within technology, which means you already understand the sector's language, pace, and priorities — that contextual knowledge is genuinely valuable and shouldn't be underestimated.
The core of this transition rests on 1 skill that directly transfer (kubernetes and docker). Your experience with kubernetes and docker as a Machine Learning Engineer gives you a genuine head start over candidates entering Cloud Engineer roles from scratch. The gaps that do exist are fillable within 6-12 months, and most can be addressed through self-directed learning, short courses, or early-career projects in the new role.
This guide covers exactly what transfers, the specific gaps you'll need to close (AWS (EC2, S3, Lambda, RDS, networking), Terraform or CloudFormation, Infrastructure-as-Code practices among them), the realistic salary impact, and a step-by-step plan for making the move from Machine Learning Engineer to Cloud Engineer in the UK market.
Why Machine Learning Engineers make this change
Machine Learning Engineers frequently reach a ceiling — whether that's salary, progression, variety, or day-to-day satisfaction — that makes them look seriously at what else their skills could unlock. Cloud Engineer work — which typically involves designing and deploying cloud infrastructure. cloud engineers spend significant time architecting systems in aws, azure, or gcp — deciding on compute (ec2, lambda), storage (s3, databases), networking, and security. decisions made here affect cost, performance, and reliability for the entire organisation. — offers a meaningfully different daily rhythm that appeals to Machine Learning Engineers looking for faster-paced, project-driven work with visible outputs. The transition isn't usually driven by a single factor — it's a combination of wanting more from your career and recognising that your Machine Learning Engineer skills open doors you hadn't previously considered.
Practically, Machine Learning Engineers are drawn to Cloud Engineer because the day-to-day work is meaningfully different while still drawing on strengths they've already developed. The mid-career earning potential for Cloud Engineers (£50,000–£75,000) compared to Machine Learning Engineer rates (£55,000–£85,000) is part of the equation — though salary shouldn't be the only reason to make a change. The strongest candidates are those genuinely interested in working with AWS (EC2, S3, Lambda, RDS, networking) and Terraform or CloudFormation and building expertise in technology.
How realistic is this career change?
This transition is realistic but requires deliberate effort. You won't walk into a Cloud Engineer role on the strength of your Machine Learning Engineer experience alone — there are specific skills and knowledge areas you'll need to build. That said, the 1 skill that transfers directly gives you a solid starting point. Expect the full transition to take 6-12 months, with the first few months focused on upskilling and the latter part on landing and settling into the new role.
The biggest risk isn't ability — it's patience. Career changers who treat this as a six-month sprint often get discouraged. Those who commit to a structured plan and accept that the first role might not be their dream position tend to succeed.
Skills that transfer directly
Kubernetes and Docker
As a Machine Learning Engineer
As a Machine Learning Engineer, you use Kubernetes and Docker in day-to-day development and problem-solving
As a Cloud Engineer
Cloud Engineers rely on Kubernetes and Docker for building and maintaining systems — your existing proficiency transfers directly
Analytical thinking
As a Machine Learning Engineer
Machine Learning Engineers develop strong analytical habits — breaking problems into components, evaluating evidence, and forming conclusions. This transfers directly to technical problem-solving
As a Cloud Engineer
Cloud Engineers apply analytical thinking to AWS (EC2, S3, Lambda, RDS, networking) and Terraform or CloudFormation, making your structured approach a genuine asset
Structured communication
As a Machine Learning Engineer
Explaining complex technology concepts to non-specialists is a skill you've practised repeatedly as a Machine Learning Engineer
As a Cloud Engineer
Cloud Engineers need to communicate technical decisions to business stakeholders, product teams, and clients — your clarity translates well
Project coordination
As a Machine Learning Engineer
Whether formally or informally, Machine Learning Engineers manage timelines, dependencies, and deliverables — that's project management in practice
As a Cloud Engineer
Most Cloud Engineer roles involve coordinating work across multiple stakeholders, so your organisational skills transfer well
Skills you'll need to build
AWS (EC2, S3, Lambda, RDS, networking)
Cloud Engineers need AWS (EC2, S3, Lambda, RDS, networking) for core aspects of the role. This isn't something you can bluff in interviews — you'll need demonstrable competence, even at a foundational level.
Start with a structured online course (Udemy, Coursera, or a bootcamp module covering AWS (EC2, S3, Lambda, RDS, networking)). Build 2-3 portfolio projects that demonstrate practical ability. Contribute to open-source projects if applicable. Most employers value demonstrated competence over formal certification.
Terraform or CloudFormation
Cloud Engineers need Terraform or CloudFormation for core aspects of the role. This isn't something you can bluff in interviews — you'll need demonstrable competence, even at a foundational level.
Start with a structured online course (Udemy, Coursera, or a bootcamp module covering Terraform or CloudFormation). Build 2-3 portfolio projects that demonstrate practical ability. Contribute to open-source projects if applicable. Most employers value demonstrated competence over formal certification.
Infrastructure-as-Code practices
Cloud Engineers need Infrastructure-as-Code practices for core aspects of the role. This isn't something you can bluff in interviews — you'll need demonstrable competence, even at a foundational level.
Start with a structured online course (Udemy, Coursera, or a bootcamp module covering Infrastructure-as-Code practices). Build 2-3 portfolio projects that demonstrate practical ability. Contribute to open-source projects if applicable. Most employers value demonstrated competence over formal certification.
CI/CD pipeline design
Cloud Engineers need CI/CD pipeline design for core aspects of the role. This isn't something you can bluff in interviews — you'll need demonstrable competence, even at a foundational level.
Start with a structured online course (Udemy, Coursera, or a bootcamp module covering CI/CD pipeline design). Build 2-3 portfolio projects that demonstrate practical ability. Contribute to open-source projects if applicable. Most employers value demonstrated competence over formal certification.
Networking (VPCs, subnets, routing)
Cloud Engineers need Networking (VPCs, subnets, routing) for core aspects of the role. This isn't something you can bluff in interviews — you'll need demonstrable competence, even at a foundational level.
Start with a structured online course (Udemy, Coursera, or a bootcamp module covering Networking (VPCs, subnets, routing)). Build 2-3 portfolio projects that demonstrate practical ability. Contribute to open-source projects if applicable. Most employers value demonstrated competence over formal certification.
Step-by-step transition plan
Expected timeline: 6-12 months
Audit your transferable skills honestly
Week 1-2Map every skill from your Machine Learning Engineer experience against Cloud Engineer job descriptions. You already have 1 directly transferable skills — document specific examples of each. Be honest about gaps rather than optimistic — this clarity drives your training plan.
Research Cloud Engineer roles and requirements
Week 2-4Read 20+ Cloud Engineer job descriptions on Indeed, LinkedIn, and sector-specific boards. Note which requirements appear in 80%+ of listings (these are non-negotiable) versus those in only a few (nice-to-haves). Talk to at least 2-3 people currently working as Cloud Engineers — LinkedIn coffee chats or industry meetups are effective for this.
Build missing skills through focused training
Month 2-4Prioritise the 2-3 skill gaps that appear most frequently in job descriptions. Online platforms (Udemy, Coursera, freeCodeCamp) offer practical, project-based learning. Focus on building evidence (projects, certificates, portfolio pieces) rather than passive learning.
Gain practical experience before applying
Month 3-6The biggest mistake career changers make is applying with theory but no practice. Build a portfolio of 3-4 projects demonstrating your new skills. Contribute to open-source projects. Freelance or volunteer for a small project. This step is what separates successful career changers from those who get stuck.
Reposition your CV and online presence
Month 5-7Rewrite your CV to lead with Cloud Engineer-relevant skills and achievements, not your Machine Learning Engineer job history. Update your LinkedIn headline to signal your target role. Write a brief career summary that frames your Machine Learning Engineer background as an asset, not a liability. Your cover letter is critical here — it needs to explain the transition story compellingly.
Target bridging roles and entry points
Month 7-10You may not land your ideal Cloud Engineer role immediately. Look for bridging positions — roles that sit between your current skill set and the target. An internal transfer within your current employer can be the easiest first step. Apply broadly, but tailor each application. Quality over quantity at this stage.
Prepare for career-changer interview questions
Ongoing throughout applicationsExpect to be asked "why are you making this change?" and "what makes you think you can do this role?". Prepare clear, concise answers that focus on what you're moving toward (not what you're leaving). Practice explaining how specific Machine Learning Engineer achievements demonstrate Cloud Engineer-relevant skills. Anticipate scepticism and address it directly with evidence.
Salary comparison
Machine Learning Engineer
Cloud Engineer
When transitioning from a mid-career Machine Learning Engineer position (£55,000–£85,000) to an entry-level Cloud Engineer role (£32,000–£44,000), expect a short-term pay adjustment. This is normal for career changes — you're trading seniority in one field for growth potential in another. The gap is typically most noticeable in the first 12-18 months.
The long-term picture is more encouraging. Experienced Cloud Engineers earn £80,000–£130,000+, and career changers who commit to the new path typically reach mid-career rates (£50,000–£75,000) within 2-4 years. Your Machine Learning Engineer background can actually accelerate this — employers value the broader perspective and professional maturity that career changers bring.
Day-to-day comparison
Your current day as a Machine Learning Engineer
As a Machine Learning Engineer, your typical day involves designing and implementing ml systems end-to-end. ml engineers own model development but also infrastructure: training pipelines, serving infrastructure, monitoring in production. this is broader than a data scientist's work — it includes engineering discipline., and building data pipelines and feature stores. data must flow reliably from sources to training and serving. ml engineers design and maintain these pipelines, often using spark, kafka, or cloud-native tools. feature stores (tecton, feast) manage reusable features.. The rhythm is shaped by technology priorities — sprint cycles, standups, and iterative delivery.
Your future day as a Cloud Engineer
As a Cloud Engineer, the day looks different: designing and deploying cloud infrastructure. cloud engineers spend significant time architecting systems in aws, azure, or gcp — deciding on compute (ec2, lambda), storage (s3, databases), networking, and security. decisions made here affect cost, performance, and reliability for the entire organisation., and infrastructure-as-code work with terraform or cloudformation. rather than manually clicking through cloud consoles, cloud engineers write code that defines infrastructure. this enables reproducibility, version control, and rapid scaling. most of the day involves writing, testing, and reviewing iac code.. The emphasis shifts to technical delivery, code reviews, and system reliability.
Repositioning your CV
Your CV needs to tell a career-change story, not just list your Machine Learning Engineer history. Lead with a professional summary that positions you as a Cloud Engineer candidate with Machine Learning Engineer experience — not the other way around. Highlight your proficiency with kubernetes and docker prominently, as these skills directly match what Cloud Engineer employers are scanning for. Every bullet point under your Machine Learning Engineer role should be rewritten to emphasise the aspect most relevant to Cloud Engineer work.
Create a "Key Skills" or "Core Competencies" section near the top that mirrors the language in Cloud Engineer job descriptions. If you've completed any training, certifications, or projects relevant to the Cloud Engineer role, give them their own section — don't bury them under your Machine Learning Engineer employment. Keep the CV to two pages maximum, and consider whether a functional (skills-based) format serves you better than a traditional chronological layout. The goal is that a hiring manager scanning for 10 seconds sees a credible Cloud Engineer candidate, not a confused Machine Learning Engineer.
How to frame your background in interviews
The interview is where career changers either win or lose. You'll face two recurring questions: "Why are you leaving Machine Learning Engineer?" and "Why Cloud Engineer?". Frame your answer around what you're moving toward, not what you're escaping. "I discovered that the aspects of my Machine Learning Engineer work I enjoy most — AWS (EC2, S3, Lambda, RDS, networking), Terraform or CloudFormation, Kubernetes and Docker — are exactly what Cloud Engineers do full-time" is stronger than "I was bored" or "I wanted better pay". Cloud Engineer interviewers specifically look for systems thinking at scale and cost awareness, so build your narrative around demonstrating these.
Prepare 4-5 examples from your Machine Learning Engineer career that directly demonstrate Cloud Engineer competencies. Your shared experience with kubernetes and docker gives you concrete examples — use them. The best career-changer examples show transferable impact: "In my Machine Learning Engineer role, I [did something] which resulted in [measurable outcome] — and this is directly comparable to how Cloud Engineers approach [similar challenge]." Don't apologise for your background or oversell it. Be matter-of-fact about what you bring and honest about what you're still building.
Qualifications and training
The technology sector is relatively qualification-agnostic — demonstrated ability matters more than certificates. That said, structured learning accelerates the transition. For Cloud Engineer roles, consider targeted online courses on platforms like Udemy, Coursera, or Codecademy. Cloud certifications (AWS, Azure, GCP), specific tool certifications, or professional body memberships can strengthen your application, but they're supporting evidence — not the main event.
A portfolio of practical projects demonstrating your skills is typically worth more than a wall of certificates. Focus your training time on building things, not just completing modules.
What successful career changers do
Treating the transition as a project with milestones, not a vague aspiration — set specific monthly targets for skills development, networking, and applications
Building genuine connections in the technology sector through industry events, LinkedIn engagement, and informational interviews with current Cloud Engineers
Being honest in interviews about your career change while confidently articulating what your Machine Learning Engineer background uniquely contributes
Maintaining financial stability during the transition — don't quit your Machine Learning Engineer role until you have a concrete plan and ideally an offer
Staying patient during the inevitable rejection phase — career changers typically need 2-3x more applications than same-sector candidates before landing the right role
Mistakes to avoid
Underselling your Machine Learning Engineer experience — career changers often feel they need to apologise for their background, when they should be framing it as an asset
Trying to make the leap in one step instead of considering bridging roles — a Cloud Engineer-adjacent position can build credibility faster than waiting for the perfect role
Copying Cloud Engineer CV templates verbatim without adapting them to tell your career-change story — hiring managers can spot a generic CV immediately
Not networking in the technology sector before applying — cold applications from career changers have a much lower success rate than warm introductions
Focusing entirely on technical skill gaps while ignoring the cultural and communication differences between technology and technology
Accepting the first offer without negotiating — career changers often feel they should be grateful for any opportunity, but you still have use, especially around your transferable experience
Frequently asked questions
Can I realistically move from Machine Learning Engineer to Cloud Engineer?
Yes — this is a moderate transition that is achievable with focused preparation. The key is identifying which of your Machine Learning Engineer skills transfer directly and addressing the specific gaps. Expect the transition to take 6-12 months from starting preparation to landing a role.
Will I need to take a pay cut to change from Machine Learning Engineer to Cloud Engineer?
In most cases, yes — at least initially. You're entering a new field where your seniority doesn't directly transfer, so your starting salary will likely be below what you currently earn as a Machine Learning Engineer. However, career changers typically reach market rate within 2-4 years, and many find the long-term earning trajectory in Cloud Engineer roles (reaching £80,000–£130,000+ at senior level) compensates for the short-term dip.
What qualifications do I need to become a Cloud Engineer?
Formal qualifications aren't always essential for Cloud Engineer roles, especially for career changers who can demonstrate relevant skills through other means. The most effective approach is targeted upskilling: identify the 2-3 most critical gaps from job descriptions and address those first. Practical evidence (projects, portfolios, voluntary work) often carries more weight than certificates alone.
How do I explain my career change in interviews?
Frame it as a deliberate, positive move — not an escape. "I discovered that the parts of my Machine Learning Engineer work I'm best at and most energised by are exactly what Cloud Engineers do full-time" is a strong opening. Back this up with 3-4 specific examples showing how your Machine Learning Engineer achievements demonstrate Cloud Engineer competencies. Be direct about your motivations and honest about what you're still learning.
Should I retrain full-time or transition while working as a Machine Learning Engineer?
For most people, transitioning while employed is more sustainable — it maintains your income, avoids a CV gap, and lets you build skills gradually. Evening courses, weekend projects, and online learning can all be done alongside your current role. If you can, negotiate reduced hours or a four-day week in your Machine Learning Engineer role to create dedicated transition time.
How long does it take to go from Machine Learning Engineer to Cloud Engineer?
The typical timeline is 6-12 months from starting active preparation to landing a Cloud Engineer role. This includes skills development, CV repositioning, networking, and the application process. Some people move faster (especially for straightforward transitions), while others — particularly those requiring formal qualifications — may take longer. Don't optimise for speed; optimise for landing the right role.
Other career changes from Machine Learning Engineer
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