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3 Tasks/ Responsibilities: - Working with Data Science teams to implement Machine Learning models into production - Design, delivery GenAI solutions - Practical and innovative implementations of LLM/ML/AI automation, for scale and efficiency - Design, delivery and management of industrialized processing pipelines - Defining and implementing best practices in ML models life cycle and ML operations/LLM operations - Implementing AI /MLOps/LLMOps frameworks and supporting Data Science teams in best practices - Gathering and applying knowledge on modern techniques, tools and frameworks in the area of ML Architecture and Operations - Gathering technical requirements & estimating planned work - Presenting solutions, concepts and results to internal and external clients - Creating technical documentation What We're Looking For: - At least 5+ years of Data engineering experience with last 3 years experience in building Data processing - At least 5+ years of experience in production-ready Python code development (e.g., microservices, APIs, etc.) - At least 3+ years of experience in production-ready ML-related code development - At least 1+ years of experience with GenAI (ChatGPT, Gemini, RAGs, prompt engineering) - Practical experience in MLOps/LLMOps tools like AzureML/AzureAI. - - Practical experience with Databricks - Good understanding of ML/AI concepts: types of algorithms, machine learning frameworks, model efficiency metrics, model life-cycle, AI architectures - Good understanding of Cloud concepts and architectures, as well as working knowledge with selected cloud services, preferably Azure or GCP - Experience in at least one of following domains: Data Warehouse, Data Lake, Data Integration, Data Governance, Machine Learning, Deep Learning, MLOps - Practical experience in Spark/PySpark and Hive within Big Data Platforms like Databricks, EMR or similar - Experience in designing and implementing data pipelines - Good communication skills - Ability to work in a team and support others - Taking responsibility for tasks and deliverables - Great problem-solving skills and critical thinking - Fluency in written and spoken English. What Will Set You Apart: - Experience in designing, programming ML algorithms, and data processing pipelines using Python - Good understanding of CI/CD and DevOps concepts, and experience in working with selected tools (preferably GitHub Actions, GitLab, or Azure DevOps) - Experience in productizing ML solutions using technologies like Spark/Databricks or Docker/Kubernetes. What we offer: - Stable employment. On the market since 2008, 1500+ talents currently on board in 7 global sites. - Office as an option model. You can choose to work remotely or in the office, depending on your location. - Flexibility regarding working hours and your preferred form of contract. - Comprehensive online onboarding program with a Buddy from day 1. - Cooperation with top-tier engineers and experts. - Unlimited access to the Udemy learning platform from day 1. - Certificate training programs. Lingarians earn 500+ technology certificates yearly. - Upskilling support. Capability development programs, Competency Centers, knowledge sharing sessions, community webinars, 110+ training opportunities yearly. - Internal Gallup Certified Strengths Coach to support your growth. - Grow as we grow as a company. 76% of our managers are internal promotions. - A diverse, inclusive, and values-driven community. - Autonomy to choose the way you work. We trust your ideas. - Create our community together. Refer your friends to receive bonuses. - Activities to support your well-being and health. - Plenty of opportunities to donate to charities and support the environment. - Contemporary office equipment. Purchased for you or available to borrow, depending on your location. .
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.