AI/ML Engineer (LLM & Generative AI) - AIENGG #NJ
Salário Estimado
R$ 9.009,00 - R$ 13.514,00
Excelente
Score da Vaga
Descrição da Vaga
the position We are seeking a highly skilled AI/ML Engineer to design, develop, and deploy advanced AI solutions, including large language model (LLM)-based applications and generative AI systems.
The ideal candidate will have hands-on experience with modern AI frameworks, cloud platforms, and scalable deployment practices.
Responsibilities • Develop and deploy AI/ML models, LLM-based solutions, and generative AI applications
Requisitos
- The ideal candidate will have hands-on experience with modern AI frameworks, cloud platforms, and scalable deployment practices
- Hands-on experience with LLMs (e.g., GPT, Claude, Llama, Gemini)
- Strong understanding of machine learning, NLP, computer vision, and model evaluation techniques
- Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, and Scikit-learn
- Experience with RAG architectures and vector databases (e.g., FAISS, Pinecone, Chroma, Azure AI Search)
- Experience with cloud platforms: Azure, AWS, or Google Cloud AI services
- Knowledge of MLOps, CI/CD for ML pipelines, and scalable model deployment
- Experience with AI copilots, automation frameworks, and agentic AI systems
- Familiarity with Docker, Kubernetes, microservices, and API orchestration
- Strong ability to communicate complex AI/ML concepts clearly to technical and non-technical stakeholders
- Python
- Machine Learning
- AI Technologies (OpenShift, React)
Responsabilidades
- Develop and deploy AI/ML models, LLM-based solutions, and generative AI applications
- Design and implement MCP servers and AI-driven architectures
- Build and optimize prompts, RAG pipelines, embeddings, and vector search solutions
- Design AI workflows using platforms such as Azure OpenAI, AWS, Google Cloud Platform, Hugging Face, LangChain, and Semantic Kernel
- Integrate AI models into production systems using APIs, Python, and cloud services
- Collaborate with business stakeholders to identify use cases and deliver measurable outcomes
- Ensure adherence to responsible AI practices, including data privacy and model governance
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