Data Scientist - Machine Learning & MLOps

at Sentiaflow

Data Scientist - Machine Learning & MLOps

Sentiaflow

Company

Home

Data Scientist - Machine Learning & MLOps

at Sentiaflow

Posted by Sonal Agarwal

3 - 8 yrs

₹20L - ₹35L / yr

Remote only

Skills

MLFlow

MLOps

Fine-tuning LLMs

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.

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.

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About Sentiaflow

Founded :

2012

Type :

Products & Services

Size :

0-20

Stage :

Bootstrapped

About

Sentiaflow is an AI engineering company headquartered in New Delhi, building production-grade AI systems for clients around the world — including Fortune 500 enterprises alongside high-growth startups. We work across finance, healthcare, and technology, helping organizations move beyond AI experimentation into real, reliable, production deployments.

We operate in three ways: embedding dedicated AI engineers directly into client teams, building custom AI solutions end-to-end — including RAG pipelines, LLM integrations, and AI agents — and designing the MLOps infrastructure that keeps these systems running reliably at scale.

Joining Sentiaflow means working on live, client-facing AI systems from day one — not internal prototypes shelved after a demo. Our engineers ship production code for organizations that depend on it, across some of the most demanding industries for AI reliability and compliance.

Python

NodeJS (Node.js)

duckdb

PostgreSQL

LangGraph

OpenAISDK

Candid answers by the company

What is the location preference of jobs?

Why should I join Sentiaflow ?

Delhi-NCR, Pune or Bangalore

Company social profiles

N/A

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