AI/ML Engineer (Agents)

TL;DR

AI/ML Engineer (Agents): Designing, developing, and deploying agentic AI systems, LLM-powered applications, and real-time inference pipelines for an AI-powered MarTech and AdTech platform with an accent on intelligent automation, personalization, and decision-making at scale. Focus on fine-tuning LLMs/SLMs, building scalable infrastructure, and implementing evaluation, A/B testing, model monitoring, and continuous improvement.

Location: Scottsdale, AZ

Company

hirify.global Inc. is building the core intelligence layer of an AI-powered MarTech and AdTech platform.

What you will do

• Design and deploy agentic AI systems using frameworks such as LangGraph, AutoGen, CrewAI.
• Build and scale LLM/GenAI applications for MarTech and AdTech use cases like content generation, campaign optimization, audience segmentation, personalization, and workflow automation.
• Fine-tune and optimize LLMs/SLMs for domain-specific tasks including classification, extraction, recommendation, and intent understanding.
• Develop and manage training and inference pipelines for production-scale ML systems.
• Build scalable infrastructure for real-time and batch inference, experimentation, and deployment.
• Partner with product, data, and engineering teams to turn prototypes and research into production-ready AI services.
• Rapidly prototype and apply emerging research to practical product use cases.

Requirements

• 3+ years of experience in AI/ML engineering, machine learning, or applied NLP roles.
• Master’s degree in Computer Science, Engineering, or a related field required; PhD preferred.
• Hands-on experience with agent-based AI frameworks such as LangGraph, AutoGen, CrewAI.
• Strong expertise in deep learning and natural language processing using PyTorch or TensorFlow.
• Experience fine-tuning and deploying foundation models such as LLaMA, Mistral.
• Strong experience with Hugging Face, LoRA, PEFT, and modern fine-tuning workflows.
• Experience with RAG pipelines, vector databases, semantic search, and embedding optimization tools such as FAISS, Pinecone, or PGVector.
• Experience deploying ML systems in cloud environments such as AWS, GCP, or Azure.
• Strong software engineering skills in Python, APIs, microservices, and containerized environments such as Docker and Kubernetes.
• Solid understanding of GPU optimization, distributed training, and inference performance tuning.

Nice to have

• Experience in MarTech, AdTech, personalization, targeting, or attribution systems.
• Experience with real-time decisioning or ad-serving systems.
• Contributions to open-source AI/LLM frameworks or published research.
• Prior experience in a startup or high-growth environment.