Senior Data Scientist

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 Scientistwith 6 to 8 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 systemsincluding 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 matrixmapped 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 promptsfor 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 hybridswhere 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 LLMsusing frameworks such as:

• Ollama, vLLM, llama.cpp, HuggingFace Transformers

• Implement quantization (4-bit/8-bit)and performance tuning.

• Support air-gapped / HIPAA-compliantinference 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 frameworksfor 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 explainabilitymatter more than flashy demos.

What We Offer

• Opportunity to work on real production AI systemsused 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.