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Role Overview:As an AI Engineer at i-Genie, you will be responsible for transforming AI prototypes and MVPs into scalable, production-grade products. Your role will involve ensuring the correct DataOps and DevOps foundations are in place, from data ingestion and transformation to final deployment and observability. You will play a crucial part in shaping i-Genies architecture strategy and infrastructure requirements, focusing on optimizing delivery speed, cost, and user experience. This role is not limited to maintaining systems but also involves influencing architectural decisions, redesigning models and processes to eliminate technical debt, and enforcing data and development standards across the team. Your infrastructure will be fundamental in supporting AI solutions used by blue-chip clients to unlock high-stakes consumer insights.Key Responsibilities:- Transform AI prototypes and MVPs into scalable, production-grade products- Ensure DataOps and DevOps foundations are in place for data ingestion, transformation, deployment, and observability- Shape i-Genies architecture strategy and infrastructure requirements- Optimize delivery speed, cost, and user experience- Influence architectural decisions and redesign models and processes to eliminate technical debt- Enforce data and development standards across the team- Support AI solutions used by blue-chip clients to unlock high-stakes consumer insightsQualifications Required:- Minimum of 5 years of experience solving real client problems through data science and data engineering, with a robust focus on NLP- Bachelors degree in Computer Science (science stream)- Proficiency in Python required; PySpark experience strongly preferred- Deep knowledge of Databricks and broad familiarity across the Azure suite- Hands-on experience deploying products into production environments using containers and Kubernetes- Solid understanding of core NLP techniques and common packages such as CoreNLP, OpenNLP, NLTK, Spacy, TextBlob, and LLM models- Experience with Large Language Models in a coding context, including pipeline integration and fine-tuning- Working knowledge of non-English text data processing- SQL skills are a plusAdditional Company Details:i-Genie offers:- Global remote work: Work from wherever you do your best thinking- Ownership & impact: Shape the architecture powering AI products used by Fortune 100 companies- Competitive compensation: Short and long-term packages designed to create wealth as the company grows- Cutting-edge problems: Real-world production challenges at the intersection of MLOps, LLMs, and NLP- Direct business impact: Your infrastructure decisions will determine how fast and how well i-Genie delivers insights at scale- Autonomy: Trust to deliver exceptional work on your terms Role Overview:As an AI Engineer at i-Genie, you will be responsible for transforming AI prototypes and MVPs into scalable, production-grade products. Your role will involve ensuring the correct DataOps and DevOps foundations are in place, from data ingestion and transformation to final deployment and observability. You will play a crucial part in shaping i-Genies architecture strategy and infrastructure requirements, focusing on optimizing delivery speed, cost, and user experience. This role is not limited to maintaining systems but also involves influencing architectural decisions, redesigning models and processes to eliminate technical debt, and enforcing data and development standards across the team. Your infrastructure will be fundamental in supporting AI solutions used by blue-chip clients to unlock high-stakes consumer insights.Key Responsibilities:- Transform AI prototypes and MVPs into scalable, production-grade products- Ensure DataOps and DevOps foundations are in place for data ingestion, transformation, deployment, and observability- Shape i-Genies architecture strategy and infrastructure requirements- Optimize delivery speed, cost, and user experience- Influence architectural decisions and redesign models and processes to eliminate technical debt- Enforce data and development standards across the team- Support AI solutions used by blue-chip clients to unlock high-stakes consumer insightsQualifications Required:- Minimum of 5 years of experience solving real client problems through data science and data engineering, with a strong focus on NLP- Bachelors degree in Computer Science (science stream)- Proficiency in Python required; PySpark experience strongly preferred- Deep knowledge of Databricks and broad familiarity across the Azure suite- Hands-on experience deploying products into production environments using containers and Kubernetes- Solid understanding of core NLP techniques and common packages such as CoreNLP, OpenNLP, NLTK, Spacy, TextBlob, and LLM models- Experience with Large Language Models in a coding context, including pipeline integration and fine-tuning- Working knowledge of non-English t .
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.