Generative AI Engineer
New
T
Talentus GlobalSoftware Outsourcing
Colombia / Uruguay / Republica Dominicana / Panama / Nicaragua / Mexico / Peru / Paraguay / Honduras / Costa Rica / Ecuador / El Salvador / Chile / Brazil / BoliviaContractMiddle
Salary not disclosed
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Job Details
- Languages
- Advanced English proficiency (C1 level or higher)
- Experience
- Minimum 3 years of experience in software engineering, machine learning, data science, or Generative AI development.
- Required Skills
- AWSPythonGCPAzureRESTful APIsGenerative AILangChain
Requirements
- Minimum 3 years of experience in software engineering, machine learning, data science, or Generative AI development.
- Bachelor's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related field preferred.
- Hands-on experience building and deploying applications using Large Language Models (LLMs) such as GPT, Claude, Gemini, Llama, or similar.
- Experience with AI orchestration frameworks such as LangChain, LlamaIndex, Semantic Kernel, or equivalent.
- Strong programming skills in Python and experience with REST APIs and backend development.
- Experience implementing RAG architectures, vector databases (Pinecone, Weaviate, Chroma, Azure AI Search, or similar), and document processing pipelines.
- Experience with cloud platforms (Azure preferred, AWS, or GCP) and AI-related services.
- Knowledge of machine learning fundamentals, model evaluation techniques, and MLOps practices.
- Experience with CI/CD pipelines, infrastructure automation, and production deployments.
- Advanced English proficiency (C1 level or higher) with strong verbal and written communication skills.
- Must have experience working for US clients
Responsibilities
- Design, develop, and deploy Generative AI solutions leveraging Large Language Models (LLMs) and multimodal AI technologies.
- Build and maintain scalable AI applications using cloud platforms such as Azure, AWS, or GCP.
- Develop and optimize Retrieval-Augmented Generation (RAG) architectures, vector databases, and knowledge retrieval systems.
- Fine-tune, evaluate, and monitor foundation models to improve performance, accuracy, and reliability.
- Implement prompt engineering strategies and AI orchestration frameworks to support business use cases.
- Collaborate with software engineering, data science, DevOps, and security teams to integrate AI solutions into production environments.
- Develop APIs, microservices, and AI-powered applications following software engineering best practices.
- Ensure compliance with AI governance, security, privacy, and responsible AI standards.
- Monitor AI workloads, model performance, and operational costs, recommending continuous improvements.
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