Sentiaflow

What we're building

We're a small, sharp team — founders, IIT/IIM folks, and industry veterans who've done this before. No bureaucracy, no layers, no meaningless standups. You'll work directly with the founding team on problems that are genuinely hard.

What we need from you

You've spent 4–8 years in AI/ML, and at least 2 of those years building real multi-agent systems — not demos, not POCs that never shipped. You've worked on at least 2 multi-agent platforms (AutoGen, CrewAI, LangGraph, custom orchestration — whatever, as long as agents were actually coordinating and not just chained prompts).

You think about:

• Agent memory, state, and context management
• Inter-agent communication and task delegation
• Failure handling, retries, and graceful degradation
• Tool use, MCP, and external system integration
• Evaluation and observability of agent behaviour

You're probably a fit if:

• You've debugged an agent loop at 11pm and found it weirdly satisfying
• You have opinions about when not to use an agent
• You care about systems that work in production, not just on your laptop

You're not a fit if:

• Your portfolio is mostly RAG pipelines and chatbots
• Your multi-agent experience is only a tutorial project or a hackathon demo

What we offer

• Best-in-class pay — we benchmark against top-tier tech and don't lowball on talent
• Direct access to founders and a senior team of IIT/IIM grads and industry veterans
• Flexible work — async-friendly, output-driven, no performative office hours
• Real technical ownership — you'll make architecture decisions, not just implement them
• Small team means your work ships fast and actually matters

Why Sentiaflow, honestly We're early. That means some things aren't figured out yet. But it also means the person we hire here will have shaped how this company thinks about agentic systems from day one. If you want to inherit a codebase and execute tickets, we're not the right place. If you want to build something from scratch with people who take the craft seriously, let's talk.

How we hire A quick intro call to get to know each other. If there's a fit, show us something you've built — a demo, a walkthrough, a system you're proud of. That's the interview. No better signal than watching someone talk about their own work.

We are looking for a talented and driven Data Scientist to join our growing Analytics team in India. In this role, you will work at the intersection of advanced machine learning, scalable MLOps infrastructure, and domain-specific healthcare analytics. You will collaborate closely with cross-functional teams to build, deploy, and maintain production-grade ML models that drive real-world impact in clinical trials and healthcare operations.

KEY RESPONSIBILITIES

End-to-End ML Development

•     Design, build, and optimize predictive models across the full ML lifecycle—from data ingestion to model serving.

•     Conduct rigorous Exploratory Data Analysis (EDA) to surface insights and drive feature engineering decisions.

•     Validate model performance using appropriate statistical techniques and domain knowledge.

MLOps & Production Deployment

•     Deploy, monitor, and maintain production-grade ML models using Databricks MLFlow endpoints and Unity Catalog.

•     Implement CI/CD pipelines for model versioning, experiment tracking, and automated retraining.

•     Ensure model reliability, observability, and performance in live production environments.

Language Models & LLM Applications

•     Apply transformer-based models (BERT, ClinicalBERT, Trial2Vec) for NLP tasks including classification, NER, and information extraction.

•     Build and maintain vector similarity search pipelines for semantic retrieval and recommendation use cases.

•     Fine-tune pre-trained models for domain-specific applications in clinical and healthcare contexts.

•     Support exploratory work around LLM integration and prompt engineering for internal tooling.

Domain-Driven Analytics

•     Apply advanced analytics within complex healthcare and clinical trial datasets—including patient records, trial protocols, and adverse event data.

•     Translate ambiguous business problems into structured analytical frameworks with measurable outcomes.

•     Partner with domain experts, product managers, and engineering teams to deliver data-driven solutions.

REQUIRED QUALIFICATIONS

Education

•     Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Bioinformatics, or a closely related field.

Experience

•     2–4 years of hands-on experience in a data science or machine learning role.

•     Demonstrable experience deploying ML models in production environments (not just prototyping).

Technical Skills

•     Strong proficiency in Python (pandas, NumPy, scikit-learn, PyTorch / TensorFlow).

•     Experience with Databricks, MLFlow (experiment tracking, model registry, endpoints), and Unity Catalog.

•     Hands-on experience with BERT-family models and Hugging Face Transformers library.

•     Familiarity with vector databases (e.g., FAISS, Pinecone, Weaviate) and embedding-based retrieval.

•     Solid understanding of SQL and working with large structured/unstructured datasets.

•     Exposure to cloud platforms (AWS / GCP / Azure) and distributed computing frameworks (Spark).

GOOD TO HAVE

•     Prior experience with clinical trial data standards (CDISC, CDASH, SDTM) or healthcare ontologies (SNOMED, ICD-10).

•     Familiarity with Trial2Vec or similar trial-to-vector embedding approaches.

•     Experience with LLM fine-tuning, RAG pipelines, or prompt engineering in a production setting.

•     Knowledge of regulatory and compliance considerations in healthcare AI (e.g., FDA guidelines, HIPAA).

•     Contributions to open-source ML projects or published research.

THIS ROLE IS NOT FOR YOU IF…

•     You have strong SQL/BI skills but limited hands-on ML modelling experience — or you’ve built models only in notebooks without ever deploying them to production.

•     Your LLM exposure is limited to API calls and prompt engineering — with no experience fine-tuning models, working with embeddings, or building vector search pipelines.