Data Scientist

Job Description 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]