AI Engineer, MLOps & Production AI

Job Description As an AI Engineer, you'll transform AI prototypes and MVPs into scalable, production, grade products , ensuring the right DataOps and DevOps foundations are in place, from data ingestion and transformation through to final deployment and observability. You'll play a central role in shaping i-Genie's architecture strategy and infrastructure requirements, optimizing for delivery speed, cost, and user experience.

This isn't just about keeping systems running. You'll influence real architectural decisions, redesign models and processes to eliminate technical debt, and enforce data and development standards across the team. Your infrastructure will directly underpin AI solutions used by blue, chip clients to unlock high, stakes consumer insights.

What You'll Work With Core Stack: Python, PySpark, SQL

AI/ML: LLMs, NLP pipelines, model fine, tuning, AI prototype productionization

NLP Libraries: CoreNLP, OpenNLP, NLTK, Spacy, TextBlob, LLM APIs

Cloud & Infrastructure: Azure suite, Databricks, containers, Kubernetes

Collaboration: Work closely with data scientists, data engineers, and product teams

What We're Looking For · 5+ years solving real client problems through data science and data engineering, with strong NLP focus

· Computer Science degree (science stream)

· Python proficiency required; PySpark experience strongly preferred, at minimum, Python mastery with a working understanding of Spark

· Deep knowledge of Databricks and broad familiarity across the Azure suite

· Hands-on experience pushing products into production environments (containers, Kubernetes)

· Solid grounding in core NLP techniques and common packages (CoreNLP, OpenNLP, NLTK, Spacy, TextBlob, 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 plus

What We Offer Global remote work: No physical office, work from wherever you do your best thinking

Ownership & impact: Shape the architecture powering AI products used by Fortune 100 companies and growing

Competitive compensation: Creative short and long, term packages designed to create wealth as we grow

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: We trust you to deliver exceptional work on your terms