Senior AI Engineer
New
J
JobgetherAI Engineering
Based in United StatesFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonSQLGCPLLMMLOpsGenerative AILangChain
Requirements
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical discipline.
- 5+ years of professional experience in AI/ML engineering or a closely related field.
- Proven experience deploying Generative AI products or features into production environments.
- Advanced proficiency in Python and shell scripting.
- Extensive hands-on experience with LLM technologies such as Google Gemini, GPT-4, LLaMA, or comparable models.
- Strong knowledge of prompt engineering, LLM fine-tuning, and embedding optimization.
- Deep experience with vector databases and semantic search technologies, including Vertex AI Vector Search, pgvector, Pinecone, or similar platforms.
- Hands-on experience with Google Cloud and Vertex AI, including designing scalable cloud-based software architectures.
- Proficiency with LangChain, LlamaIndex, or comparable AI orchestration frameworks.
- Strong understanding of software engineering principles, clean code, maintainability, testing, and the full software development lifecycle.
- Knowledge of data engineering concepts and SQL.
Responsibilities
- Architect and build end-to-end Generative AI applications using Python, LangChain, LlamaIndex, and Google Cloud technologies.
- Design and implement advanced Retrieval-Augmented Generation pipelines and semantic search solutions using technologies such as Google Cloud Vector Search, Vertex AI Vector Search, Pinecone, or pgvector.
- Lead LLM and embedding fine-tuning initiatives to improve performance for specialized business domains and use cases.
- Develop and manage agentic workflows capable of automating complex, multi-step reasoning and business processes.
- Collaborate directly with clients to understand business requirements, recommend innovative AI capabilities, and translate technical concepts into practical solutions.
- Take AI features from prototype through production, applying disciplined software engineering practices throughout the development lifecycle.
- Apply MLOps practices to deploy, monitor, maintain, and continuously improve AI models and services.
- Engineer solutions for production constraints including latency, scalability, reliability, error handling, observability, and cost efficiency.
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