AI Engineer Jobs

As an AI Engineer, you will be at the forefront of groundbreaking technological advancements. AI Engineers are responsible for developing and implementing artificial intelligence solutions that drive innovation across industries such as healthcare, finance, and robotics. With a strong foundation in machine learning, neural networks, and data modeling, AI Engineers create intelligent systems that can interpret complex data, learn from patterns, and make decisions. The demand for AI Engineers is rapidly growing, with companies seeking individuals who can advance AI technologies to new levels. AI Engineers typically earn a yearly salary range of $90,000 to $150,000 depending on experience and location. To excel in this field, aspiring AI Engineers should master tools such as TensorFlow, PyTorch, scikit-learn, and have strong programming skills in Python or Java. Despite high demand, work-life balance in this field can be challenging, given the fast-paced nature of technology development.

About this role

Design, develop, and deploy AI agents and end-to-end GenAI solutions to solve complex business and engineering challenges.
Collaborate with domain experts and stakeholders to understand processes and translate requirements into AI-driven solutions.
Build and integrate solutions using Azure AI services, including Azure OpenAI, Azure AI Foundry, Azure Machine Learning, AI Search, and Cognitive Services.
Develop Retrieval-Augmented Generation (RAG) pipelines, AI copilots, chatbots, and agentic workflows leveraging enterprise knowledge sources.
Implement prompt engineering, tool/function calling, workflow orchestration, and multi-agent frameworks using LangChain, Semantic Kernel, or similar technologies.
Deploy, monitor, and optimize AI applications in production while ensuring security, governance, scalability, and performance.
Establish LLMOps/MLOps practices for model deployment, evaluation, monitoring, and continuous improvement.

Requirements

Strong programming skills in Python.
Hands-on experience with Azure AI ecosystem (Azure OpenAI, Azure AI Foundry, Azure ML, AI Search, Cognitive Services).
Experience building LLM-based applications, AI agents, RAG solutions, and GenAI-powered applications.
Knowledge of prompt engineering, vector databases, semantic search, and orchestration frameworks (LangChain, Semantic Kernel, AutoGen, etc.).
Strong analytical, problem-solving, and stakeholder management skills.
Familiarity with cloud-native development, APIs, and MLOps/LLMOps practices.

About Unison Group

Embark Your Global Digital Transformation Journey

Team

Gibran Duarte

Jiin Choi

Rajini Navaretnam

Design, develop, and deploy AI agents and end-to-end GenAI solutions to solve complex business and engineering challenges.
Collaborate with domain experts and stakeholders to understand processes and translate requirements into AI-driven solutions.
Build and integrate solutions using Azure AI services, including Azure OpenAI, Azure AI Foundry, Azure Machine Learning, AI Search, and Cognitive Services.
Develop Retrieval-Augmented Generation (RAG) pipelines, AI copilots, chatbots, and agentic workflows leveraging enterprise knowledge sources.
Implement prompt engineering, tool/function calling, workflow orchestration, and multi-agent frameworks using LangChain, Semantic Kernel, or similar technologies.
Deploy, monitor, and optimize AI applications in production while ensuring security, governance, scalability, and performance.
Establish LLMOps/MLOps practices for model deployment, evaluation, monitoring, and continuous improvement.

Requirements

Strong programming skills in Python.
Hands-on experience with Azure AI ecosystem (Azure OpenAI, Azure AI Foundry, Azure ML, AI Search, Cognitive Services).
Experience building LLM-based applications, AI agents, RAG solutions, and GenAI-powered applications.
Knowledge of prompt engineering, vector databases, semantic search, and orchestration frameworks (LangChain, Semantic Kernel, AutoGen, etc.).
Strong analytical, problem-solving, and stakeholder management skills.
Familiarity with cloud-native development, APIs, and MLOps/LLMOps practices.