lever
AI/ML & Forward Deployed Engineer
jobgether
Remote, AUS
Full-time
Remote
Discovered 2 weeks ago
Machine learningAI/ML engineeringPythonDeep learningNatural language processingForecasting
Free
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Machine learningAI/ML engineeringPython
Role Overview
This position is listed on behalf of a partner company seeking an AI/ML and Forward Deployed Engineer based in Australia.
The role builds and deploys AI, machine learning, and generative AI solutions from initial concept through production.
The engineer combines software engineering, machine learning, and MLOps or LLMOps expertise to create scalable and reliable AI products.
The work spans experimentation, evaluation, deployment, monitoring, and continuous improvement, with emphasis on security, governance, observability, and data quality.
Key Skills for This Role
Machine learningAI/ML engineeringPythonDeep learningNatural language processingForecasting
Full Job Posting
Role Overview
This position is listed on behalf of a partner company seeking an AI/ML and Forward Deployed Engineer based in Australia.
The role builds and deploys AI, machine learning, and generative AI solutions from initial concept through production.
The engineer combines software engineering, machine learning, and MLOps or LLMOps expertise to create scalable and reliable AI products.
The work spans experimentation, evaluation, deployment, monitoring, and continuous improvement, with emphasis on security, governance, observability, and data quality.
Accountabilities
- Design, develop, and deploy machine learning, AI, and GenAI solutions from proof of concept through production.
- Build and optimize models and applications for deep learning, NLP, forecasting, classification, regression, and anomaly detection.
- Develop production-grade GenAI applications using LLMs, RAG pipelines, embeddings, retrieval optimization, reranking, and prompt engineering.
- Design and implement evaluation frameworks for model quality, reliability, relevance, and performance.
- Build scalable AI services and integrations using REST and gRPC APIs and event-driven architectures.
- Establish MLOps and LLMOps practices covering deployment, automation, versioning, monitoring, and lifecycle management.
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