AI/NLP Engineer

LEO Technologies, LLC

full-time

Posted on: 9/6/2025

Origin: • 🇺🇸 United States • California

✨ AI Apply

💰 $130,000 - $150,000 per year

Mid-LevelSenior

AirflowApacheAWSAzureCloudElasticSearchElixirGrafanaKafkaPythonPyTorchReactTerraformTypeScript

About the role

Design, build, and optimize AI-powered solutions using LLMs, RAG pipelines, semantic search, GraphRAG, and Agentic AI architectures.;Implement and experiment with the latest advancements in large-scale language modeling, including prompt engineering, model fine-tuning, evaluation, and monitoring.;Collaborate with product, backend, and data engineering teams to define requirements, break down complex problems, and deliver high-impact features aligned with business objectives.;Inform robust data ingestion and retrieval pipelines that power real-time and batch AI applications using open-source and proprietary tools.;Integrate external data sources (e.g., knowledge graphs, internal databases, third-party APIs) to enhance the context-awareness and capabilities of LLM-based workflows.;Evaluate and implement best practices for prompt design, model alignment, safety, and guardrails for responsible AI deployment.;Stay on top of emerging AI research and contribute to internal knowledge-sharing, tech talks, and proof-of-concept projects.;Author clean, well-documented, and testable code; participate in peer code reviews and engineering design discussions.;Proactively identify bottlenecks and propose solutions to improve system scalability, efficiency, and reliability.

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.;5+ years of hands-on experience in applied AI, NLP, or ML engineering (with at least 2 years working directly with LLMs, RAG, semantic search and Agentic AI).;Deep familiarity with LLMs (e.g. OpenAI, Claude, Gemini), prompt engineering, and responsible deployment in production settings.;Experience designing, building, and optimizing RAG pipelines, semantic search, vector databases (e.g. ElasticSearch, Pinecone), and Agentic or multi-agent AI workflows in in large scale production setup.;Exposure to GraphRAG or graph-based knowledge retrieval techniques is a strong plus.;Strong proficiency with modern ML frameworks and libraries (e.g. LangChain, LlamaIndex, PyTorch, HuggingFace Transformers).;Ability to design APIs and scalable backend services, with hands-on experience in Python.;Experience building, deploying, and monitoring AI/ML workloads in cloud environments (AWS, Azure) using services like AWS SageMaker, AWS Bedrock, AzureAI, etc.;Familiarity with MLOps practices, CI/CD for AI, model monitoring, data versioning, and continuous integration.;Demonstrated ability to work with large, complex datasets, perform data cleaning, feature engineering, and develop scalable data pipelines.;Excellent problem-solving, collaboration, and communication skills;Proven record of shipping robust, high-impact AI solutions, ideally in fast-paced or regulated environments.