Data Scientist – LLM & Applied AI

Job Description

Position:	Data Scientist – LLM & Applied AI
Experience:	6 to 8 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 6 to 8 years of experience who is deeply proficient in Large Language Models (LLMs) — both open-source and commercial — and 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.

8. Hands-on experience in designing and implementing AI strategies, developing ML/AI models, and translating business requirements into scalable, data-driven AI solutions.

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.