AI Developer (AdTech)

TL;DR

AI Developer (AdTech): Design, build, and deploy production-grade AI systems powered by LLMs and modern ML for high-impact AdTech project with an accent on multi-agent systems, LLM pipelines, and cloud-native infrastructure. Focus on optimizing workflows using LangChain and LangGraph, integrating NLP/LLM capabilities into applications, and monitoring model performance in production.

Location: Remote-first with flexible working hours

Company

hirify.global builds cloud-first, AI-driven solutions for Silicon Valley startups and US Fortune 500 enterprises as an AWS Advanced Consulting Partner and CNCF member.

What you will do

• Design and implement AI/ML models and LLM-based systems for production
• Build and optimize workflows using LangChain and LangGraph
• Develop and deploy scalable AI solutions in cloud environments
• Work with stakeholders to translate business needs into AI-driven features
• Integrate NLP and LLM capabilities into user-facing applications
• Prototype rapidly, iterate based on feedback, monitor, and improve production performance
• Contribute to code reviews, documentation, and knowledge sharing

Requirements

• English: B2–C1
• Strong Python skills and solid ML fundamentals
• Experience with Databricks (PySpark, Notebooks, Unity Catalog)
• Hands-on with LangChain and LangGraph
• Experience with MLflow (training, tracking, deployment)
• Good SQL knowledge (MySQL / Databricks SQL)
• Building and deploying LLM-powered applications
• Understanding of LLM guardrails, multi-agent systems, AI workflows, and cloud-based AI systems
• Strong problem-solving and analytical skills

Nice to have

• Evaluating or fine-tuning LLMs (Llama, GPT, Gemini)
• RAG pipelines, vector databases, or mixed-model architectures
• AI assistants, NLQ systems, or AdTech/Marketing analytics
• MLOps, CI/CD, GitHub workflows
• Kubernetes, serverless, or cloud platforms (AWS/Azure)
• NestJS, Prisma, or backend integrations
• Reducing AI/ML technical debt or AI copilots in IDEs

Culture & Benefits

• Remote-first setup with flexible hours in a distributed, engineering-driven team
• Competitive USD compensation (B2B or employment)
• Paid leave, sick days, holidays
• Workspace budget ($2500) + software coverage
• Paid certifications (AWS, Kubernetes, ML) and English lessons
• Access to international conferences (CNCF, KubeCon)
• Real collaboration, ownership, minimal bureaucracy