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The work Machine Learning Engineers make machine learning and AI models reproducible, deployable, scalable, and supportable. They build the path from training data and experimentation to a versioned model service that can be released, monitored, retrained, and retired without guesswork. The role centers on the model lifecycle and the platform beneath it. Machine Learning Engineers automate training and validation, manage features and model artifacts, optimize inference, implement machine learning operations (MLOps), and watch for changes in data, behavior, performance, reliability, and cost. They create the shared tooling that lets data scientists and application engineers move models into production safely. What you'll build · Reproducible training, validation, tuning, and retraining pipelines with versioned data, code, parameters, environments, and model artifacts. · Model-serving systems and APIs designed for appropriate latency, throughput, availability, scaling, and rollback. · Feature pipelines, feature stores, model registries, lineage records, approval workflows, and automated release controls. · Monitoring and alerting for data quality, drift, model performance, fairness, infrastructure health, latency, and cost. · Reusable libraries, templates, environments, and delivery pipelines that give data scientists a tested path from experiment to production. Who you are You are comfortable at the seam between modeling and software engineering. You can inspect a model, harden a pipeline, diagnose a production failure, and improve the platform so the same class of problem is easier to prevent next time. You value repeatability over heroics. You work closely with data scientists on model behavior, data engineers on reliable inputs, AI Engineers on application integration, and platform and security teams on the environment in which the model runs. What you bring · Strong programming and software-engineering practice, including testing, version control, packaging, automation, and production debugging. · Working knowledge of model development, evaluation metrics, feature engineering, data splitting, tuning, and the limits of different modeling approaches. · Experience with training and inference pipelines, containers, cloud or on-premises compute, artifact management, and automated deployment. · Practical MLOps experience with model registries, lineage, reproducibility, monitoring, drift analysis, retraining, release controls, and rollback. · The ability to balance model quality with reliability, interpretability, security, privacy, latency, throughput, and cost. About OPEN Data Jobs OPEN Data Jobs connects AI, data, and software professionals with critical roles, primarily in the federal sector. Registering with ODJ can put your profile in view for multiple positions across several clients. Register for Machine Learning Engineer Role Click Apply below to register for Machine Learning Engineer Role. Requirements What openings may require An opening may emphasize predictive models, computer vision, natural language models, ranking, anomaly detection, recommender systems, edge inference, generative AI model operations, or an enterprise ML platform. Some openings will focus more on model development, while others will focus more on serving and platform engineering. Specific openings may name Python, SQL, Java, model frameworks, distributed-processing tools, cloud ML services, container orchestration, graphics processing units, feature stores, model registries, experiment tracking, or infrastructure as code. OPEN Data Jobs will identify the required depth for each opening Benefits Compensation, benefits, work location, and employment terms are set for each specific opening and will be stated with that opening
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.