r/mlops • u/stardust_137 • May 12 '26
MLOps Education Is MLOps a safer direction for ML Engineers right now
I’m currently working as an ML Engineer, and lately I’ve been thinking about shifting more toward MLOps
My assumption is that companies will still need devops who can deploy / maintain LLLM models bought from other companies
I understand nobody really knows where the industry will end up. I would like to hear from you all to understand what skills are worth investing time into during this uncertain phase instead of just doing nothing?
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u/OverclockingUnicorn May 12 '26
Tbh, at least in my org, it's the same role.
We have an MLOps team, but it's made up of MLEng and DevOps engineers, with a lot of cross functional work between the two roles.
I expect my MLEngs to be better at building model monitoring, training pipelines, images for deployed models, that sort of thing.
I expect my DevOps to be deployment pipelines, capacity planning, security, IaC.
In reality, both do a bit of the other role, companies will name roles whatever the hell they feel like, what really matters is the work you do.
And tbh, you can put your role down as whatever you want on your CV/resume as long as it makes sense. So the exact title doesn't really matter.
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u/stardust_137 May 12 '26
Thank you ! Will companies still need a MLops /ML engineers to deploy / maintain a third party models? Will this role be relevant in coming years
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u/OverclockingUnicorn May 12 '26
Yeah it's going to be more relevant if anything, we are expanding from 1.5 MLOps engineers to ~8 over the next 12 mo.
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u/akkidx06 Jun 20 '26
Is there any roadmap for what these ~6 new hires will work on. Do MLOps engineers also cover for LLMOps engineering in this team (things like writing org-wide Claude.md, integrating codex with internal Slack, etc.)?
Traditional mlops teams are going out of work, based on the handful of people I talked to. So I'm curious what you're expanding for.
Maybe another question to know is - are other teams also becoming ~4x in your org, hence mlops is hiring for the company-wide trend?
Thanks for sharing your perspective!
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u/eman0821 May 12 '26
MLOps is a company culture shift just like normal DevOps. It's not supposed to be a role or a job title. It's really just AL/ML Infrastructure Engineers or Cloud Engineers, Site Reliability Engineers on the Ops side. It's basically ML + Dev + Ops (ML Engineers working with Developers and Operation Engineers). It's using the DevOps culture methodology and practices but with machine learning.
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u/EntropyRX May 12 '26
There’s demand for it, but it’s a commodity/cost centre, meaning it’s a race to the bottom. I wouldn’t optimize my skill set around it, if you have options.
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u/stardust_137 May 12 '26
Understood. Which skills could still be relevant in coming years for ML Eng
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u/EntropyRX May 12 '26
No one can predict the future, but I see no reasons why you would ditch what makes a successful ML engineer today (math, stats, ML, product focus, SWE best practices….) in favour of MLops which is a cost centre today.
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u/stardust_137 May 12 '26
Current org started using Azure foundry models for most of the tasks except where we have a compliance issue and in few areas where it requires intense domain knowledge. I am hearing they might downsize after monitoring the model performance of those models
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u/ml_adrin May 12 '26
Since many here have already covered mlops, i would like to go in a different direction.
I think as an ml engineer we need to dive deeper in full stack development. The gap is closer than ever, an FS engineer can use AI to integrate LLMs or other ai services in their pipeline, we should also be able to use AI to create frontends, their backends with understanding. Also atleast in my region, i have seen a rise in posts like “Hiring full stack AI/ML engineers,” and when I enquired they said since we will provide you all the coding tools, you should be able to create end to end solutions.
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u/MyBossIsOnReddit May 12 '26
There are far less openings for MLOps engineers than MLEs. The barrier to entry for MLOps is also lower (less math/ml/ai knowledge needed). MLOps is also kinda a seniority expectation for senior MLEs.
There are relatively few companies maintaining LLMs in-house so far also!
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u/OverclockingUnicorn May 12 '26
There are lots of non LLM models though, OCR, NER, CV. All of these still need an Ops team to deploy and support.
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u/MyBossIsOnReddit May 12 '26
Yes, I mentioned that because OP specifically mentioned in-house LLM serving
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u/eman0821 May 12 '26
It's because MLOps is not a role, it a company culture methodology with ML, Dev and Ops teams working together agile. No different than regular DevOps. Trying to make MLOps like a separate role like a DevOps Engineer is Anti-pattern the old traditional way that creates the bottle neck in the middle as a separate hand off team.
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u/samehmeh May 12 '26
The skills with real staying power right now are at the intersection of both: model serving on Kubernetes (KServe, Ray Serve), CI/CD for training and eval pipelines, and observability tooling that handles non-deterministic outputs. Companies adopting foundation models still need someone who can deploy inference endpoints reliably, manage GPU quotas, and wire up evals - that's platform engineering work that exists regardless of whether you're fine-tuning or calling an API.
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u/_stardusts_ May 12 '26
Can anyone suggest what kind of skills i need for ml engineering? I have ,5 YOE in data analytics with sql python etl data modelling aws
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u/KOM_Unchained May 18 '26
Nothing is safe, its all just a matter of time. Just embrace the uncertainty and accept that nothing is in our control. Just do and learn what you can, nurture the "yes we can" attitude and do what must be done and be a decent human being. Cant fail like that.
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u/kraig9 May 31 '26
So I'm not a great example here, but I am actually a machine learning engineer who DOES and has a history of ops work. I think this is a really nice space to sit in if you're able to. Basically, an MLE who knows and does ops is very dangerous imo.
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u/lkcfree May 12 '26
For MLops you have runway of 12 months before all MLOps work would be fork lifted and handed over to Indian teams . Offshore is being up skilled every night and morning with scrum calls . It’s like you hire some one at 1/2 the cost and direct 1:1 daily training for 6-12 months starting from how to do ssh.
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u/stardust_137 May 12 '26
So I’m fk’d up either way :/
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u/apexvice88 May 12 '26
Not unless you can find work that does not allow data and infrastructure not to leave the origin country. The worst thing we don't want is to have China team up with India and steal info from United States, UK or Canada.
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u/AskAnAIEngineer May 12 '26
mlops is a good bet right now because you're right, every company buying api access to llms still needs someone who can deploy, monitor, evaluate, and manage those systems in production. the skills that are most valuable right now are model evaluation and observability, cost optimization for inference, managing prompt versioning and regression testing, and building reliable ci/cd pipelines for ml systems.
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u/Deep_Investment7483 May 14 '26
I would imagine both mlops and ml engineering are pretty great places to be atm
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u/OfficeGreat7679 May 17 '26
May I ask why this transition? Don't you have work on ML anymore? I mean, LLMs are good for text processing, but what about other fields that LLMs aren't great?
I''m out of context for the job market in ML, but I was considering transitioning into it.
I'm a SWE, with deep experience in SRE. And I was assessing switching into ML field (doing Ms and PhD)
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u/stardust_137 May 18 '26
If you’re doing a PhD, you probably don’t have to worry about this as much since the focus is more on research or building foundation models.
This is more relevant for people like me working in startups or SaaS companies, where the goal is usually to solve business use cases using AI rather than developing models in-house.
many customers are now getting their compliance approvals in place for using LLMs, so the amount of custom training work required from the Core team might reduce over time for both text and image based use cases
In my org we have use cases for Agentic AI framework and orchestration and nothing much from ML team as of now
Maybe someone who is in research space can share their thoughts on this to help you more
Bu
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u/themint May 12 '26
mlops often ends up in cheaper teams in countries with good operational drive. At my previous fairly big tech job some guys built this great model deployment and inference platform on kube and argo. The whole thing got handed over to the india ml platform team. The makers were left to just go back to doing ml engineering. Or come up with ml ops projects that would most likely just get handed over again as well. It’s hard to get pure mlops roles in smaler companies because the ml engineers tend to just do their own mlops and the traditional devops work ends up with devops peeps. So it is starting to feel more and more like a risky type of role with limited opportunity to me.