Intern AI Engineer

Company Description

Inferyx is an enterprise data intelligence platform that provides integrated data management, a versatile business rules engine, advanced analytical capabilities, and strong data governance. Based on a unified, metadata-driven architecture, Inferyx helps organizations harness the power of their data to drive informed decision-making. The platform is designed to streamline data processes, ensuring efficiency and compliance while enabling innovation and insights.


Role Description

This is a remote internship role for an AI Engineer. The AI Engineer will work on developing and refining AI models, focusing on tasks such as pattern recognition and natural language processing (NLP). Daily responsibilities may include designing, training, and testing neural networks, software development, and collaborating with the team to solve complex problems using AI technologies.


Key Responsibilities


• Design and implement machine learning and deep learning models

• Develop and deploy agentic AI systems capable of autonomous decision-making

• Build AI agents using orchestration frameworks and LLM APIs such as those from OpenAI

• Implement planning, reasoning, memory, and tool-use capabilities in AI agents

• Design Retrieval-Augmented Generation (RAG) systems and integrate vector databases

• Build scalable AI pipelines for training and real-time inference

• Deploy models and agents to cloud environments

• Monitor performance, evaluate outputs, and continuously improve agent reliability

• Ensure AI systems are secure, safe, and aligned with responsible AI principles


Required Skills & Qualifications


• Bachelor’s or Master’s degree in Computer Science, AI, or related field

• Strong Python programming skills

• Experience with ML/DL frameworks:

• TensorFlow

• PyTorch

• Experience building AI agents using frameworks such as:

• LangChain

• AutoGen

• Understanding of:

• Large Language Models (LLMs)

• Prompt engineering and fine-tuning

• Tool integration (APIs, function calling)

• Memory systems (short-term, long-term, vector-based memory)

• Multi-agent collaboration architectures

• Experience with cloud platforms (AWS, Azure, or GCP)

• Knowledge of Docker, Kubernetes, CI/CD, and MLOps


Preferred Qualifications


• Experience designing autonomous task-executing agents

• Familiarity with evaluation frameworks for LLMs and agents

• Knowledge of distributed systems (Spark, Ray)

• Experience with reinforcement learning or decision systems

• Understanding of AI safety, guardrails, and model alignment


Soft Skills


• Strong systems thinking

• Ability to design complex workflows

• Excellent communication and documentation skills

• Continuous learning mindset

  • field