Job Description
Position: Data Scientist LLM & Applied AI
Experience: 23 Years
Employment Type: Full-Time
Domain: Healthcare AI / Digital Health / SaaS Platforms
Reporting To: CTO
Role Summary
We are seeking a highly hands-on Data Scientist with 23 years of experience who is deeply proficient in Large Language Models (LLMs)both open-source and commercialand has strong expertise in prompt engineering, applied machine learning, and local LLM deployments.
This role is not purely academic. The ideal candidate will work on real-world AI systems including AI Frontdesk, AI Clinician, AI RCM, multimodal agents, and healthcare-specific automation, with a focus on production-grade AI, domain-aligned reasoning, and privacy-aware architectures.
Key Responsibilities
1. LLM Research, Evaluation & Selection
- Evaluate, benchmark, and compare open-source LLMs (LLaMA-2/3, Mistral, Mixtral, Falcon, Qwen, Phi, etc.) and commercial LLMs (OpenAI, Anthropic, Google, Azure).
- Select appropriate models based on latency, accuracy, cost, explainability, and data-privacy requirements.
- Maintain an internal LLM capability matrix mapped to specific business use cases.
2. Prompt Engineering & Reasoning Design
- Design, test, and optimize prompt strategies:
- Zero-shot, few-shot, chain-of-thought (where applicable)
- Tool-calling and function-calling prompts
- Multi-agent and planner-executor patterns
- Build domain-aware prompts for healthcare workflows (clinical notes, scheduling, RCM, patient communication).
- Implement prompt versioning, prompt A/B testing, and regression checks.
3. Applied ML & Model Development
- Build and fine-tune ML/DL models (classification, NER, summarization, clustering, recommendation).
- Apply traditional ML + LLM hybrids where LLMs alone are not optimal.
- Perform feature engineering, model evaluation, and error analysis.
- Work with structured (SQL/FHIR) and unstructured (text, audio) data.
4. Local LLM & On-Prem Deployment
- Deploy and optimize local LLMs using frameworks such as:
- Ollama, vLLM, llama.cpp, HuggingFace Transformers
- Implement quantization (4-bit/8-bit) and performance tuning.
- Support air-gapped / HIPAA-compliant inference environments.
- Integrate local models with microservices and APIs.
5. RAG & Knowledge Systems
- Design and implement Retrieval-Augmented Generation (RAG) pipelines.
- Work with vector databases (FAISS, Chroma, Weaviate, Pinecone).
- Optimize chunking, embedding strategies, and relevance scoring.
- Ensure traceability and citation of retrieved sources.
6. AI System Integration & Productionization
- Collaborate with backend and frontend teams to integrate AI models into:
- Spring Boot / FastAPI services
- React-based applications
- Implement monitoring for accuracy drift, latency, hallucinations, and cost.
- Document AI behaviors clearly for BA, QA, and compliance teams.
7. Responsible AI & Compliance Awareness
- Apply PHI-safe design principles (prompt redaction, data minimization).
- Understand healthcare AI constraints (HIPAA, auditability, explainability).
- Support human-in-the-loop and fallback mechanisms.
Required Skills & Qualifications
Core Technical Skills
- Strong proficiency in Python (NumPy, Pandas, Scikit-learn).
- Solid understanding of ML fundamentals (supervised/unsupervised learning).
- Hands-on experience with LLMs (open-source + commercial).
- Strong command of prompt engineering techniques.
- Experience deploying models locally or in controlled environments.
LLM & AI Tooling
- HuggingFace ecosystem
- OpenAI / Anthropic APIs
- Vector databases
- LangChain / LlamaIndex (or equivalent orchestration frameworks)
Data & Systems
- SQL and data modeling
- REST APIs
- Git, Docker (basic)
- Linux environments
Preferred / Good-to-Have Skills
- Experience in healthcare data (EHR, clinical text, FHIR concepts).
- Exposure to multimodal AI (speech-to-text, text-to-speech).
- Knowledge of model evaluation frameworks for LLMs.
- Familiarity with agentic AI architectures.
- Experience working in startup or fast-moving product teams.
Research & Mindset Expectations (Important)
- Strong inclination toward applied research, not just model usage.
- Ability to read and translate research papers into working prototypes.
- Curious, experimental, and iterative mindset.
- Clear understanding that accuracy, safety, and explainability matter more than flashy demos.
What We Offer
- Opportunity to work on real production AI systems used in US healthcare.
- Exposure to end-to-end AI lifecycle: research prototype production.
- Work with local LLMs, agentic systems, and multimodal AI.
- High ownership, visibility, and learning curve.
If you're ready to take on a challenging and rewarding leadership role in the evolving world of healthcare IT, we want to hear from you! Please share your CV at [Confidential Information]
If you're ready to take on a challenging and rewarding role in the evolving world of healthcare IT, we want to hear from you! Please share your CV at [HIDDEN TEXT]
