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SR Agentic AI / LLM Engineer
Hybrid - Chicago, IL (Open for relocation)
Looking for a SR Agentic AI / LLM Engineer to work onsite in Chicago. They need SR Level – someone who has built RAG solutions from scratch.
Design, develop, and maintain scalable web services using FastAPI or Flask frameworks.
Write efficient, reusable, and modular Python code to support API-driven LLM applications.
Lang Chain & Supporting Frameworks:
Implement Lang Chain to build custom pipelines for document indexing, retrieval, and summarization.
Integrate Lang Chain’s RAG capabilities with other components like vector stores and retrievers to support real-time querying and document processing.
RAG Pipelines:
Architect and deploy Retrieval-Augmented Generation (RAG) systems for chatbots, knowledge systems, and other generative AI applications.
Optimize RAG systems for speed, accuracy, and scalability across multiple use cases.
Vector Stores & Retrievers:
Work with vector databases like Pinecone, Chroma, FAISS, or Milvus to store and manage embeddings.
Implement retrievers and re-rankers to improve query efficiency, ensuring high-quality and relevant outputs for users.
AWS Cloud Deployment:
Deploy and manage LLM-based applications on AWS, leveraging services such as Lambda, EC2, S3, EKS, and RDS.
Ensure the scalability, availability, and reliability of deployed applications.
Dashboards and Monitoring (Optional):
Create monitoring dashboards using tools like Grafana or Tableau for real-time system monitoring, analytics, and performance insights.
Experimentation with Generative AI:
Research and integrate the latest advancements in generative AI technologies.
Experiment with fine-tuning and adapting large language models (like GPT, BERT) for new, innovative use cases.
Required Technical Skills
Python proficiency, especially with web frameworks like FastAPI or Flask.
Strong experience with Lang Chain and associated libraries.
Proven expertise in building and optimizing RAG pipelines.
Proficiency in using vector databases (e.g., Pinecone, FAISS).
Experience with retrievers and re-rankers.
Solid understanding of AWS services (Lambda, EC2, RDS, etc.).
Knowledge of SQL and NoSQL databases.
Familiarity with dashboarding tools such as Grafana and Tableau.
Soft Skills
Problem-solving: Ability to handle complex and dynamic challenges with AI solutions.
Collaboration: Experience working in multidisciplinary teams (data scientists, DevOps, etc.).
Adaptability: Eagerness and passion to keep up with the latest AI advancements and incorporate them into solutions.
Communication: Excellent verbal and written communication skills to convey technical information to both technical and non-technical stakeholders.
SR Agentic AI / LLM Engineer
Hybrid - Chicago, IL (Open for relocation)
Looking for a SR Agentic AI / LLM Engineer to work onsite in Chicago. They need SR Level – someone who has built RAG solutions from scratch.
Design, develop, and maintain scalable web services using FastAPI or Flask frameworks.
Write efficient, reusable, and modular Python code to support API-driven LLM applications.
Lang Chain & Supporting Frameworks:
Implement Lang Chain to build custom pipelines for document indexing, retrieval, and summarization.
Integrate Lang Chain’s RAG capabilities with other components like vector stores and retrievers to support real-time querying and document processing.
RAG Pipelines:
Architect and deploy Retrieval-Augmented Generation (RAG) systems for chatbots, knowledge systems, and other generative AI applications.
Optimize RAG systems for speed, accuracy, and scalability across multiple use cases.
Vector Stores & Retrievers:
Work with vector databases like Pinecone, Chroma, FAISS, or Milvus to store and manage embeddings.
Implement retrievers and re-rankers to improve query efficiency, ensuring high-quality and relevant outputs for users.
AWS Cloud Deployment:
Deploy and manage LLM-based applications on AWS, leveraging services such as Lambda, EC2, S3, EKS, and RDS.
Ensure the scalability, availability, and reliability of deployed applications.
Dashboards and Monitoring (Optional):
Create monitoring dashboards using tools like Grafana or Tableau for real-time system monitoring, analytics, and performance insights.
Experimentation with Generative AI:
Research and integrate the latest advancements in generative AI technologies.
Experiment with fine-tuning and adapting large language models (like GPT, BERT) for new, innovative use cases.
Required Technical Skills
Python proficiency, especially with web frameworks like FastAPI or Flask.
Strong experience with Lang Chain and associated libraries.
Proven expertise in building and optimizing RAG pipelines.
Proficiency in using vector databases (e.g., Pinecone, FAISS).
Experience with retrievers and re-rankers.
Solid understanding of AWS services (Lambda, EC2, RDS, etc.).
Knowledge of SQL and NoSQL databases.
Familiarity with dashboarding tools such as Grafana and Tableau.
Soft Skills
Problem-solving: Ability to handle complex and dynamic challenges with AI solutions.
Collaboration: Experience working in multidisciplinary teams (data scientists, DevOps, etc.).
Adaptability: Eagerness and passion to keep up with the latest AI advancements and incorporate them into solutions.
Communication: Excellent verbal and written communication skills to convey technical information to both technical and non-technical stakeholders.