Sr AI/ML Engineer
Posted 3 days ago by HR Pundits
Negotiable
Undetermined
Remote
Remote
Summary: The Senior AI/ML Engineer role focuses on designing, benchmarking, and optimizing text embedding and vector search solutions for large-scale clinical data, particularly in the healthcare domain. The position requires extensive experience in AI/ML and data engineering, with an emphasis on building high-performance pipelines and evaluating AI models for intelligent search across patient records. The role is remote and involves ensuring compliance with PHI/HIPAA standards. Candidates should have a strong background in AI/ML technologies and cloud platforms.
Key Responsibilities:
- Develop and benchmark text chunking and embedding strategies
- Evaluate embedding models (OpenAI, Cohere, SBERT, etc.)
- Design and optimize vector search systems
- Build scalable pipelines for large datasets (up to billions of documents)
- Measure performance across cost, speed, scalability, and accuracy
- Support near real-time indexing and high query workloads
- Ensure compliance with PHI/HIPAA and enterprise security standards
Key Skills:
- Strong experience in AI/ML and NLP (embeddings, transformers)
- Hands-on with LLMs, RAG, and vector databases
- Proficiency in Python and data frameworks (Spark/Databricks)
- Experience with cloud platforms (Azure/AWS/Google Cloud Platform)
- Knowledge of distributed systems and high-throughput pipelines
Salary (Rate): £104,000 yearly
City: undetermined
Country: undetermined
Working Arrangements: remote
IR35 Status: undetermined
Seniority Level: undetermined
Industry: IT
Role: Senior AI/ML Engineer (Vector Store & Retrieval Systems) TechM221906
Location: Remote
10+ years in AI/ML or Data Engineering (healthcare domain preferred) to design, benchmark, and optimize text embedding and vector search solutions for large-scale clinical data. This role focuses on building high-performance pipelines and evaluating AI models to support intelligent search across patient records.
Key Responsibilities:
- Develop and benchmark text chunking and embedding strategies
- Evaluate embedding models (OpenAI, Cohere, SBERT, etc.)
- Design and optimize vector search systems
- Build scalable pipelines for large datasets (up to billions of documents)
- Measure performance across cost, speed, scalability, and accuracy
- Support near real-time indexing and high query workloads
- Ensure compliance with PHI/HIPAA and enterprise security standards
Required Skills:
- Strong experience in AI/ML and NLP (embeddings, transformers)
- Hands-on with LLMs, RAG, and vector databases
- Proficiency in Python and data frameworks (Spark/Databricks)
- Experience with cloud platforms (Azure/AWS/Google Cloud Platform)
- Knowledge of distributed systems and high-throughput pipelines
Understanding of healthcare data and PHI compliance