Artificial Intelligence Engineer

Senior AI Engineer

Location:

Company Overview

TheHireHub.ai is a recruiter-first AI hiring platform built by recruitment professionals with over 20+ years of industry experience. Designed to automate and elevate the entire hiring lifecycle, the platform leverages a multi-agent AI architecture to manage job creation, sourcing, outreach, screening, evaluation, interview scheduling, and analytics. TheHireHub.ai combines applied AI, workflow automation, and human-in-the-loop intelligence to help organizations hire faster, smarter, and with higher precision.

Role Overview

We are seeking a Senior AI Engineer to design, build, and scale the AI systems that power TheHireHub.ai’s multi-agent recruiting platform. This role focuses on architecting intelligent workflows using large language models (LLMs), retrieval systems, and agent orchestration frameworks to deliver production-grade AI capabilities across hiring use cases.

You will be responsible for building end-to-end AI pipelines that enable autonomous and semi-autonomous agents to reason, evaluate candidates, generate outreach, manage screening, and support decision-making—while ensuring performance, cost efficiency, and reliability at scale.

Key Responsibilities

• Architect and implement LLM-driven multi-agent systems for recruitment workflows including job analysis, candidate matching, outreach, screening, and evaluation.
• Design and maintain modular AI pipelines combining retrieval, reasoning, scoring, and response generation layers.
• Build and orchestrate agent workflows using frameworks such as LangChain, LangGraph, and Model Context Protocol (MCP).
• Develop scalable backend APIs and microservices to expose AI capabilities across the platform.
• Implement and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases and embedding models.
• Evaluate, fine-tune, and deploy open-source and proprietary models for text and structured data tasks.
• Optimize AI systems for latency, throughput, token usage, and cost efficiency.
• Collaborate closely with product, backend, frontend, and data teams to embed AI features into user-facing workflows.
• Build internal tooling to accelerate experimentation, prompt iteration, agent evaluation, and deployment.
• Stay current with emerging AI research, frameworks, and agent architectures to guide platform evolution.

Essential Skills & Technologies

Core AI & ML

• Strong hands-on experience with LLMs (OpenAI GPT, Anthropic Claude, Llama, Mistral, Gemini, DeepSeek, etc.).
• Deep understanding of prompt engineering, function calling, tool use, and context management.
• Experience building agent-based systems using LangChain, LangGraph, and MCP (Model Context Protocol).
• Solid understanding of RAG architectures, vector databases (Pinecone, Weaviate, FAISS, Chroma), and embedding strategies.
• Familiarity with model fine-tuning, evaluation, and serving using frameworks like Hugging Face, vLLM, or Ollama.

Engineering & Architecture

• Strong proficiency in Python (FastAPI preferred); Node.js experience is a plus.
• Experience designing microservices, async systems, and event-driven architectures.
• Cloud deployment experience (AWS, GCP, Azure, or serverless environments).
• Databases: MongoDB / PostgreSQL / Redis.
• Strong understanding of API design, security, and scalable system architecture.

Optimization & Observability

• Experience optimizing AI systems for latency, reliability, and cost.
• Token usage management and cost optimization for LLM-based workflows.
• Monitoring, logging, and tracing using tools such as Prometheus, Grafana, ELK, or OpenTelemetry.

Additional Plus

• Experience with LangServe, LlamaIndex, Haystack, or Autogen frameworks.
• Exposure to multi-agent reasoning, evaluation matrices, or autonomous workflows.
• Familiarity with MLOps tools (MLflow, Kubeflow, Weights & Biases).
• Understanding of multimodal AI (documents, resumes, structured hiring data).
• Frontend exposure (React / TypeScript) for integrating AI-driven UX.

What You’ll Bring

• 5+ years of experience building and deploying AI/ML systems in production environments.
• Strong background in NLP, generative AI, or applied machine learning.
• Ability to balance innovation with system performance, reliability, and scalability.
• Strong engineering fundamentals and experience shipping real-world AI products.
• A builder’s mindset—curious, hands-on, and driven to create high-impact AI systems.