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Why Hiring · Australia

AI/ML Forward Deployed Engineer

entry_levelfull timePosted 29 days ago
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Stack mentioned

llmragagentic-aipythonpytorchlangchaintensorflowgitawsazuregcpdockermlopsllamaindexsqlartificial-intelligencemachine-learninggenerative-aidata-sciencehugging-face

Company Description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an AI/ML-focused Forward Deployed Engineer to join their remote team and help customers turn advanced Artificial Intelligence (AI), Machine Learning (ML), and Generative AI technologies into practical, production-ready solutions. The role involves building and deploying AI/ML solutions, developing LLM applications, RAG pipelines, AI agents, and predictive models, integrating models with APIs and cloud platforms, and working closely with customers to solve complex technical and business problems through Applied AI, Machine Learning Engineering, Data Science, and AI-driven solutions.

About the Role

We are looking for an AI/ML-focused Forward Deployed Engineer who enjoys working at the intersection of machine learning, software engineering, and real-world problem solving. You will work closely with customers and internal engineering teams to understand technical challenges, prototype AI solutions, and help turn successful concepts into reliable applications. This role is highly hands-on and offers exposure to modern AI technologies, including LLMs, RAG, AI agents, predictive modeling, and cloud-based ML systems.

What You’ll Do

Build and deploy AI/ML solutions using Python and modern machine learning frameworks

Develop LLM-powered applications, RAG pipelines, AI agents, and intelligent workflows

Prototype and evaluate machine learning solutions based on customer requirements

Work with frameworks and libraries such as PyTorch, scikit-learn, Hugging Face, LangChain, or similar tools

Prepare, transform, and analyze structured and unstructured data for AI/ML applications

Integrate AI models with APIs, databases, cloud platforms, and existing customer systems

Experiment with different models, prompts, retrieval strategies, and AI workflows

Evaluate model performance and improve the reliability and effectiveness of deployed AI solutions

Support the deployment, monitoring, and optimization of AI/ML applications

Collaborate directly with customers to understand technical requirements and translate them into practical AI solutions

Communicate technical concepts and solution recommendations clearly to both technical and non-technical stakeholders

Work with internal engineering teams to move successful prototypes toward production

Requirements

Bachelor’s degree or equivalent practical experience in Computer Science, Data Science, Artificial Intelligence, Engineering, or a related field

Strong Python programming skills

Foundational knowledge of machine learning and deep learning

Hands-on experience with at least one ML framework such as PyTorch, TensorFlow, or scikit-learn

Familiarity with LLMs, Generative AI, embeddings, RAG, prompt engineering, or AI agents

Understanding of APIs, Git, and basic cloud concepts

Experience working with structured and/or unstructured datasets

Strong analytical and problem-solving skills

Ability to translate business or customer requirements into technical solutions

Strong written and verbal communication skills

Comfortable working in a fast-paced, collaborative, and customer-facing environment

Nice to Have

Experience deploying ML models or AI applications to production

Familiarity with AWS, Azure, or Google Cloud

Experience with Docker or basic MLOps workflows

Knowledge of vector databases, semantic search, or embedding-based retrieval

Familiarity with frameworks such as LangChain, LangGraph, LlamaIndex, or similar

Experience building AI/ML projects outside of academic coursework

Familiarity with REST APIs, SQL, or backend development

Understanding of model evaluation, monitoring, and responsible AI practices

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