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

- 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).
- Solid 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.

📌 Senior Data Scientist (Ahmedabad)
🏢 OmniISM Technologies
📍 Ahmedabad