AI Engineer
- Location
- Global
- Workplace
- Remote
- Employment
- Full Time
- Salary
- —
Posted 23d ago
The employer’s full description could not be read from their board. This is a summary of the posting — follow the apply link for the original.
Responsibilities
- Design, build, and deploy intelligent solutions using ML, Deep Learning, and Generative AI
- Work across the AI/ML lifecycle from experimentation to production deployment
- Collaborate with engineering, product, and data teams to turn business challenges into AI solutions
Requirements
- Strong proficiency in Python
- Experience developing production-ready AI/ML applications and services
- Solid experience with Machine Learning including model development, evaluation, and optimization
- Hands-on experience building applications with Generative AI and LLMs
- Experience with Deep Learning concepts, neural networks, and modern model architectures
- Practical experience with PyTorch and/or TensorFlow
- Experience with NLP including text processing, embeddings, semantic search, classification, or generation
- Hands-on experience designing and implementing RAG pipelines and vector-based retrieval
- Experience integrating AI/ML APIs, foundation models, and third-party AI services
- Understanding of MLOps and model deployment including versioning, monitoring, CI/CD, and production ML workflows
- Experience with data engineering concepts for AI/ML workloads
- Strong software engineering fundamentals with focus on maintainability, scalability, testing, and code quality
Preferred
- Experience with LLM fine-tuning, prompt engineering, and model evaluation
- Experience with vector databases, embeddings, and semantic retrieval systems
- Exposure to AI agents, tool/function calling, or multi-agent architectures
- Experience with cloud-based AI/ML platforms, containerization, and orchestration technologies
- Experience implementing AI observability, guardrails, responsible AI, or model governance
- Experience optimizing AI applications for performance, latency, scalability, and cost
Skills
- Python
- Machine Learning
- Deep Learning
- Generative AI
- Large Language Models
- PyTorch
- TensorFlow
- Natural Language Processing
- RAG
- Vector Databases
- MLOps
- CI/CD
- LLM Fine-tuning
- Prompt Engineering
- AI Agents
- Cloud AI Platforms
- Containerization
- Orchestration
- AI Observability
- Responsible AI
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