MLOps Engineers Lost in a Vague Role
Individuals transitioning into or working within MLOps roles consistently express feeling overwhelmed and lost due to the vastness and complexity of the field. Many enter with incorrect assumptions, often believing it's a direct extension of DevOps, and struggle to navigate the diverse aspects of model deployment, infrastructure, and real-time pipelines. A lack of clear guidance and established roadmaps exacerbates this confusion.
SOURCES (60)
“I want to pivot to MLE but currently work as a data engineer with 3.5 years of relevant experience and about to finish my master's in computer science. My technologies are: AWS tech stack, Airflow, Python, SQL, Snowflake, Terraform, Confluent. I worked in data science…”
“Hi all, I am looking to go for MS/PhD in ML. My research interests are in label efficient ML, ML for pathology and I want to pursue a career in research. However, since I donot have any formal research experience, I am not positive about securing a PhD admission and will be also okay doing a research-focussed MS. Browsing this and similar subreddits has made me anxious about the competitiveness of grad admissions, please help me realistically evaluate my chances at good MS programmes. I would ap”
“Hi all, I am looking to go for MS/PhD in ML. My research interests are in label efficient ML, ML for pathology and I want to pursue a career in research. However, since I donot have any formal research experience, I am not positive about securing a PhD admission and will be also okay doing a research-focussed MS. Browsing this subreddit has made me anxious about the competitiveness of grad admissions, please help me realistically evaluate my chances at good MS programmes. I would appreciate blun”
“I have 10 years of experience in IT managing marketing automation tools (sfmc, non-coding role) and was recently laid off from a ₹50 LPA package in India. The current market is rough, and the offers I'm seeing cap out around ₹30-35 LPA. Since I've been unemployed for two months, I'm planning to accept an interim role to stay afloat while preparing a 2-year exit plan to move abroad for commercial growth. I've heard that in countries like Japan, individuals with limited technical s”
“Hi everyone, I’m currently working as a data/data warehouse engineer, and I’m seriously considering transitioning into the AI + education / EdTech product space. My background is a little unusual. I studied education-related subjects in college, but my professional career eventually went into data engineering. Most of my current work involves understanding business systems, analyzing databases and data models, working with SQL, and figuring out how business processes are represented in data. Ove”
“have to do this as a prerequisite for an interview. This isn’t for an MLE position at all but I need to perform well. i’m a few years postgrad and my current role really only uses sql so I have like 2 days to relearn all of this ML stuff from college. I understand the concepts but it says there are 2 coding questions where you have to code and implement an ML solution. Basically trying to figure out if I’m fucked or not because there’s really no study guide besides 1 practice question. has anybo”
“I'm a Linux sysadmin (RHEL, Ansible, GitLab CI, ~3 years) about to move onto a small AWS team. Long term I want to end up SRE or platform engineer, and I'm working through RHCSA and SAA-C03 on the side. From where I sit the market looks split in two: generic sysadmin work is getting squeezed, and everything interesting is behind Kubernetes, IaC and real production experience. But that's me reading job ads, not living it. submitted by /u/Natural_Pool_5493 [link] [com”
“Key Skills Microsoft Azure AI Azure OpenAI Azure AI Foundry Azure AI Services Generative AI (GenAI) Azure AI Search Enterprise AI Integration Roles & Responsibilities Deploy, configure, and optimize Azure AI Services, Azure OpenAI, Azure AI Foundry, and Azure AI Search . Migrate AI workloads, models, and applications from on-premises or other clouds to Azure. Design and implement GenAI, Conversational AI, Speech, Vision, and Document Intelligence solutions. Integrate Azure AI with enterprise”
“Let me get this straight, you have no experience and want to pick a niche? Your goal right now should be getting anything you can get. Doesn't matter if its backend, ML, etc. the skills you learn are transferrable. Focus on getting an internship, that is the top priority, not specializing.”
“It's not going to be replaced, at least by LLM's. I think the market will eventually somewhat bounce back, but as for right now it's a bit rough. You have to really stand out especially as a new grad or junior developer to get a job. Not impossible though (local jobs are going to be much easier to land). That's not to say it's easy though. Doable? Yes. Easy? No.”
“Hi everyone, I'm planning to pursue a Master's in AI/ML for the 2027 intake and would really appreciate some advice from people who have gone through a similar transition. My undergraduate background is in Aerospace Engineering from India, but over the course of my degree I've developed a strong interest in Python, AI/ML, data science and software/AI applications , and I'm looking to transition into this field for my Master's. And I'm not having a plan to come back home a”
“Entry level DE is mostly SQL and Python anyway, you're closer than you think. Build a couple portfolio projects with dbt and Airflow, throw them on github, and start applying to junior roles that list those. The market is rough but someone who actually enjoys pipelines stands out more than the bootcamp crowd.”
“Hi there, everyone. I am a 28M, and a few years ago I got a bachelor's in Math and a master's in Data Science from my local university a year ago. I don't have an internship or relevant data science experience. I am embarrassed to admit that I am still stuck looking for entry-level positions. Does anyone know what to do? I feel ashamed and embarrassed that it is still taking me this long to get something while my peers are succeeding in their relevant fields and are younger than me.”
“Recent AI/ML MSc grad (India), finishing up. Looking for honest perspectives on a decision I've mostly made. Background: ~1.6 years across three internships — mostly NLP/LLM systems (speech pipelines, RAG, LLM observability) and some geospatial ML (foundation model evaluation on satellite imagery). My core strength/interest is NLP/speech/LLM and geospatial. The offer: A full-time role at an established IIoT / startup (~40 people). The work is more on the signal processing / time-series / ano”
“Hi everyone, I just passed the OA for Capital One. I applied to a few roles, with my background (5 YoE) in financial engineering. It used to be more ML, now more data science-ish. I taught myself SQL and R on the job, since my prior experience was with Python. The recruiter in C1 is asking me if I want to move forward pursuing a full stack SWE role they have open, or hold off for now. My experience is really just with Python, SQL and R. I never worked with Java, and definitely not front end stuf”
“Deciding between two offers in India, would appreciate perspectives from anyone who's worked at either company or in similar roles. Background: ~2-3 years of experience in ML/AI at a startup, currently working on production AI systems. Option 1 — Amazon Ads, Data Scientist L4 (Bangalore) Ads-adjacent DS team ~₹26L base, ~₹32L CTC (bonus + RSUs) Hiring manager said modeling work is ~30-40% of the role, varies a lot by problem statement Unsure how much is meaningful ML/AI work vs. traditional”
“Data science, economics. Or my path (not a typical path): philosophy. I’ve applied as much from history and mythology and philosophy in my career as I have economics and data science. But the latter are directly relevant.”
“so...since you aren't LLM (i hope!), there's no need to take this seriously and try to address every single question so please reply to any of these: 1. Do you program/develop ML apps? 2. if yes what language do you use: python or...else? 3. Is the architecture you're using transformers based or something else ? 4. do you train the model you're using from scratch? 5. are you familiar with some of these exotic ML-related keywords: softmax, convolution, HDC, QKV, SSM, yudkowsky (lol),hidden statem”
“in the age of AI domain expertise will become the more important thing, especially if you want to a leadership role”
“I have been thinking of studying for the CDMP exam- I am a data analyst but want to transition data governance and in the future to AI governance. Would the CDMP or some other certifications help? submitted by /u/zkhan15 [link] [comments]”
“I’m trying to figure out when I should start applying. I don’t have internship experience yet, but I feel like my technical level is already above a typical internship and getting closer to junior/mid-level applied AI work. What I’m mainly missing is production experience. My current skills include Python, RAG, document parsing/chunking, embeddings, Qdrant, hybrid retrieval, LLM APIs, validation/repair pipelines, LangGraph, tool calling, state/memory, and agentic AI. Should I start applying now,”
“What is the hardest part of ML system design in production? Not modeling — the system around the model. For example: Data → Features → Training → Evaluation → Deployment → Serving → Monitoring → Feedback Where do you see the most difficult engineering problems in practice? A few candidates: Training/serving skew Feature freshness GPU utilization Online inference latency Experimentation Data quality Model drift Feedback loops Multi-tenancy Cost Curious to hear what has caused the most pain in sys”
“Can’t express enough when constructing an app to start with data and the API contracts. 90% of the work you’ll do in your life as a dev is just talking to databases.”
“Just graduated with B.S. in CS and starting a ML masters program this fall, both from a T20 CS school in the US. My 3 swe internships were at defense companies and by random chance every project I worked on was building ML products. My main concern is my internship experiences were all research & development in a sense. None of the things I worked on were live services or things that got deployed. Everything was essentially just a proof of concept where my intern team was given free reign fo”
“Hi, So our company was working to setup mlops systems for training models, I work there as an ML Engineer and started working on this pipeline, we got many parts ready but the start is the issue. Context: We have a client in dubai, aka a bank, it processes millions of data daily. Now what we wanted to do is to move the client customers data from production to lower environment for training but with masking for PII's. But here is the limit it should not leave client region in cloud i.e. Dubai”
“Data engineering still hot. AI enablement in data engineering also hot. Upskill into databricks, aws, general rest API and IaC design patterns, and be at least working with mcp servers and ai productivity like claudecode. What not to do- anything that doesn’t get you more ai-enabled. I can’t stress that enough. I lead a data engineering team in an enterprise setting and this is the #1 paradigm shift in anything engineering.”
“I have a bachelors in Computer Science and a masters in Data Science but the truth is I did both mostly to pass the courses and get the degrees. I would learn things for exams and assignments but never really practiced enough. So now I have two degrees but very little actual programming or coding ability. For the past few years I've mostly been outsourcing projects and that worked well for me financially. I also make some income from Facebook monetization and a few extensions I've built”
“Ive done a lot in my career from HFT, Linux Kernel Dev and now AI Infra etc... At this point I see software going in two or three directions. Extreme Domain expertise Ph.d wise to progress AI models Frontier Model Development AI infra like what Im doing Its extremely depressing that as a society we constantly devalue every human trait. And now knowledge/ expertise that many of us have spent our career building is totally commoditized. If we are at this point, where hard work and craftsmanship ca”
“Hey all, Thought I'd share where I've got to, partly to be honest about it and partly because I'd like some outside perspective. I'm an ML engineer with a full-time job. Everything below happened on nights and weekends over about a year. First attempt was real estate with a friend's friend, finding houses with legal problems, he'd sort the legal side and we'd sell them on. Never got off the ground. Two busy people isn't a company. Second was a QA platform for mobi”
“Data engineering can stay remote, but employer type matters more than niche. Smaller SaaS companies, remote-first analytics platforms, and regulated teams needing governance often care more about output than desk time. I would aim for analytics engineering or data governance with strong SQL, dbt, and cloud skills, then filter hard for actual remote-first companies, not hybrid-friendly ones.”
“Im a data engineer with 4 years of experience, and In getting pretty bored with my current job. Most of the code is generated by AI now, so my dayis mostly just maintaining pipelines. I ve been offered an internal move to Data Product Management. Its for one of the biggest projects in the company and will be built on Databricks. They told me they need someone with a solid data engineering background who can translate business requirements effectively. However, I have a few doubts Am I just going”
“How did you qualify for the AI FDE role? Did you complete any certs or personal projects?”
“Background: I'm doing an MSc in CS (aiming for the ML track), with ~3 years of prior industry experience in cloud security before starting. Long-term goal is AI/ML research/academia. I am an international student far from home. I also went through a terrible breakup recently and some medical issues, I was never a bad student but now I feel like I am just falling behind and can't recover. If I fail once more I will be kicked out of the university. First semester I failed 1 exam. Second se”
“I'm a Data Analyst, and I'm trying to bridge a gap in my Data Science understanding. I know the concepts behind classical ML reasonably well but I want to understand what actually happens to an ML project in a real production environment from start to finish. I want someone to walk me through a real project in terms of: We use this application/tool to do this → it produces this output/file/artifact → that goes into this tool or system → then this team works on it → then it moves to the n”
“I'm looking for a real-world ML project to add to my portfolio. Could you suggest some projects that would help me stand out in the ML job market? #it #MLengineer #technology submitted by /u/Main-Organization487 [link] [comments]”
“I have a question about “domain expertise” as a component of future knowledge worker requirements. How does one gain such expertise in a context where thinking is expected to be delegated to AI (shifting from problem solving to question asking, as noted in this paper)?How does one learn to pose the right questions when basic ones are rarely “manually” answered? that is, without an AI assistant’s helpThis pattern appears in schools, where AI interferes with human development that typically demand”
“I'm on a 3-person team: 1 data scientist, and 2 engineer (including myself). Between the both of us engineers, we have maybe a year of experience total with strictly Python. This is for a prototype AI app / tool. I am approaching about 2 years of IT experience and had to pick up docker, gitlab ci, and software engineering for this project. I built out our deployments with docker compose using customized env files (ignored via git with a template available) and makefile commands. We usually d”
“My background is in STEM and programming but I want to leave that field. I want to specialize either in Cybersecurity or in Data Analysis, my only goals being stability and growth opportunities. I'm reading everywhere that the job market in both fields is bad so I don't know which one to choose and how awful it would be trying to do both until I start working somewhere. My previous field has a worse job market and now I'm working on retail (stable, but no chance of growth and definit”
“Actually im in TCS as a asst sys eng … im in a project where im doing playwright testing …but i need to switch to ai ds ML … and im also doing projects based on it so after two after or a year how to switch to a company has ML background Or data sided company … so additionaly what ive to learn to get a better switch submitted by /u/Salty_War_6780 [link] [comments]”
“So I've been building an MLOps project for the past couple months and finally got everything working locally. Now I need to actually deploy it and I'm going in circles trying to figure out the best approach. The problem is I have 4 containers running together via Docker Compose. Works perfectly on my machine but the moment I think about cloud deployment the economics get weird. Azure gives me $100 through the GitHub Student Pack which sounds like a lot until you realize 4 containers runn”
“Just start trying to build something or a clone of a software that you like. Build it without AI for bonus points.”
“I’ve been looking for resources to prepare for ML System Design interviews , particularly case studies that include complete, end-to-end solutions . The book Machine Learning System Design Interview: An Insider's Guide by Alex Xu and Ali Aminian was an excellent resource when I used it around three years ago. It provides a structured framework and several detailed case studies with detailed solutions. My question is: are the solutions in this book still sufficiently current and comprehensive”
“I'm currently trying to build my career toward AI/ML engineering, and this is something I've been struggling with for a while. I'm genuinely obsessed with AI. I can spend hours learning and building things around: \- Machine Learning / Deep Learning \- Transformers \- LLMs \- RAG \- AI Agents \- Embeddings & Vector DBs \- Model deployment \- AI system design If something breaks in a RAG pipeline, I actually enjoy figuring out why it broke. But when I sit down to do LeetCode... My”
“Devops or data analyst? ADMIN DON'T REMOVE !! Age 27 just turned Experience - technical support/helpdesk/IT support 3 years Financial strength - lower middle class No generational wealth Average in studies Likes work life balance Confused between devops and data analyst:- Cloud/Devops - I like the course and tools and all. I am not much into coding side. I like course. Like the money Problem - on call. This has been killing me. If there would be on call rotation in every company then it'”
“I’m a fresher graduating in AI/ML and want to target **ML Engineer / AI Engineer / GenAI roles at good product companies**, while keeping SWE/backend roles as a fallback. My current plan is to build a stronger end-to-end profile around **ML + GenAI + backend/software engineering + deployment**, rather than just doing generic ML projects. I already have a full-stack ML project and plan to build another production-style project involving things like LLMs/RAG, APIs, databases, Docker, AWS and CI/CD”
“You can also have been working in the field for 4+ years and not have shipped anything yet. The tech moves so fast, it’s often obsolete as soon as it hits the market.”
“Hello everyone, I'm a fresher majoring in Computer Science Engineering specialized in Ai. And I need some guidance and I've got so many doubts. Here they are: How do I build my skills? Is it really necessary to know multiple languages for an internship? What were the hardships you guys faced when you were a newbie? Is it possible that I might face them too? Will the college I am in affect the opportunities I get outside college? How do I use Git and Github? I've tried searching it up”
“It’s either that your experience is in frontier labs or you have PhD + publish on ai topics. Anything masters is going to provide either outdated, too fundamental and theoretic and doesn’t add value if you have CS bachelor with 5+yoe. You’d learn more practical knowledge from building project than a whole year of school.”
“Hello, I'm in my 3rd year of my Computer Science major (specializing in AI/ML). Looking back, I honestly regret not starting earlier and feel like I wasted my first two years without a clear direction. I want to turn things around and seriously break into MLOps. I actually looked into it and want to go for MLOps. Since I'm essentially starting fresh I’m feeling a bit overwhelmed by Docker, Kubernetes, CI/CD, feature stores, model monitoring and all. If you were in my shoes today how woul”
“I have ~2.5 years of experience working as a software engineer in a banking software company, followed by a 3-year maternity career break. I’m now trying to restart my career and have around 6 months to become job-ready , with the goal of getting a job in 2027. I’m confused between Salesforce, Data Analytics/BI, and other moderately technical roles. I don’t want something extremely coding-heavy, but I’m comfortable with technical work and would like to leverage my previous experience. I’ve also”