Overview
On Site
$$70 per hour
Accepts corp to corp applications
Contract - W2
Contract - Independent
Contract - months contract
Skills
LLM
NLP
RAG
Azure
Panda
numpy
HuggingFace
OpenAI
LoRA
PEFT
JSON
Job Details
Job Summary (List Format):
- Contribute to building a production-ready Generative AI platform for Johnson & Johnson Innovative Medicine as part of an emerging R&D team.
- Design and implement scalable Retrieval-Augmented Generation (RAG) pipelines for large-scale document processing and semantic chunking from cloud sources (Azure Blob, AWS S3).
- Integrate and tune embedding models and vector databases (e.g., Milvus) for high-performance document retrieval.
- Develop hybrid retrieval systems (BM25 and vector search) and optimize retrieval performance using key metrics (MRR, recall@k).
- Apply and fine-tune large language models (LLMs) for complex NLP tasks such as named-entity recognition, question answering, and summarization.
- Build and optimize prompt engineering solutions, ensuring structured outputs and reducing model hallucinations.
- Develop, containerize, and deploy agent-based microservices (FastAPI, Azure Functions); define Infrastructure as Code (Terraform/ARM).
- Establish CI/CD workflows (GitHub Actions) for automated testing and deployment, including monitoring and alerting for system performance and SLA compliance.
- Profile and optimize system performance and cost (batching, caching, early-stopping), while producing high-quality documentation and architecture diagrams.
- Ensure security and compliance with industry regulations (HIPAA/GxP), including data encryption and PII redaction.
- Collaborate onsite three days a week (hybrid role; must be within driving distance).
- No visa sponsorship available for this position.
- Contribute to building a production-ready Generative AI platform for Johnson & Johnson Innovative Medicine as part of an emerging R&D team.
- Design and implement scalable Retrieval-Augmented Generation (RAG) pipelines for large-scale document processing and semantic chunking from cloud sources (Azure Blob, AWS S3).
- Integrate and tune embedding models and vector databases (e.g., Milvus) for high-performance document retrieval.
- Develop hybrid retrieval systems (BM25 and vector search) and optimize retrieval performance using key metrics (MRR, recall@k).
- Apply and fine-tune large language models (LLMs) for complex NLP tasks such as named-entity recognition, question answering, and summarization.
- Build and optimize prompt engineering solutions, ensuring structured outputs and reducing model hallucinations.
- Develop, containerize, and deploy agent-based microservices (FastAPI, Azure Functions); define Infrastructure as Code (Terraform/ARM).
- Establish CI/CD workflows (GitHub Actions) for automated testing and deployment, including monitoring and alerting for system performance and SLA compliance.
- Profile and optimize system performance and cost (batching, caching, early-stopping), while producing high-quality documentation and architecture diagrams.
- Ensure security and compliance with industry regulations (HIPAA/GxP), including data encryption and PII redaction.
- Collaborate onsite three days a week (hybrid role; must be within driving distance).
- No visa sponsorship available for this position.
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