Senior Data Scientist
Reads like a ML Engineer role: putting models into production.
Remote, Telangana, India
- Pay
- Pay not listed
- Work setting
Remote — work setting source
Listed location: Remote, Telangana, India
Read the full posting- Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting
What you’ll work on
Full postingDesign, build, and productionize ML, generative AI, and agentic systems
Implement MLOps workflows using MLflow for reliability and performance tracking
Own the full lifecycle of AI products from experiment to live production
From the employer’s posting
Design, build, and productionize large-scale machine learning, generative AI and agentic systems and models.
Implement robust MLOps workflows using MLflow to ensure model reliability, scalability, and performance tracking.
Take ownership of the full lifecycle of AI products, ensuring models move successfully from experimental stages to live production environments.
What you’ll bring
All qualificationsCore experience
- 4+ years of hands-on experience in data science, ML, GenAI, and MLOps
- Proven track record of moving models beyond prototype into production
- Expert-level MLflow and model deployment and monitoring on Databricks
- Experience with Databricks Spark, Lakebase, and Vector Search
- Experience with at least one agent framework such as LangChain, LangGraph, or DSPy
Qualification wording
4+ years of hands-on experience in Data Science, Machine Learning, GenAI, and MLOps.
Proven experience productionizing models (must have moved models beyond the sandbox/prototype phase).
Expert-level proficiency with MLflow and model deployment and monitoring on Databricks.
Experience with Databricks Spark, Lakebase, Vector Search.
Experience with at least one agent development framework like Langchain, Langgraph, DSPy etc.
Tools in this posting
- Databricks
- MLflow
Source — Tool mentions in context
The ideal candidate possesses deep technical expertise in MLOps and GenAI. Work Location: Hyderabad, Bangalore, Mumbai, Delhi, Gurgaon. Work Mode: Work From Office 5 days a week End-to-End Development: Design, build, and productionize large-scale machine learning, generative AI and agentic systems and models. Operational Excellence: Implement robust MLOps workflows using MLflow to ensure model reliability, scalability, and performance tracking. Production Deployment: Take ownership of the full lifecycle of AI products, ensuring models move successfully from experimental stages to live production environments. Experience: 4+ years of hands-on experience in Data Science, Machine Learning, GenAI, and MLOps. Track Record: Proven experience productionizing models (must have moved models beyond the sandbox/prototype phase). Technical Stack: Required: Expert-level proficiency with MLflow and model deployment and monitoring on Databricks. Required: Experience with Databricks Spark, Lakebase, Vector Search. Required: Experience with at least one agent development framework like Langchain, Langgraph, DSPy etc. Good to have: Hands on experience with Databricks Apps, AI/BI Dashboards, Genie etc. Good to have: Databricks Asset Bundle (DAB), Git, experience with large repo management (GitHub, ADO etc.)
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Sep 30, 2026 · 28 days ago
- Recorded sightings
- 32
- Last seen by us
- Oct 28, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Your next step
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- Check the listed location, eligibility and core experience before starting.
- Ask the employer about the pay range before committing time to the process.
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Job description
The ideal candidate possesses deep technical expertise in MLOps and GenAI. Work Location: Hyderabad, Bangalore, Mumbai, Delhi, Gurgaon. Work Mode: Work From Office 5 days a week End-to-End Development: Design, build, and productionize large-scale machine learning, generative AI and agentic systems and models. Operational Excellence: Implement robust MLOps workflows using MLflow to ensure model reliability, scalability, and performance tracking. Production Deployment: Take ownership of the full lifecycle of AI products, ensuring models move successfully from experimental stages to live production environments. Experience: 4+ years of hands-on experience in Data Science, Machine Learning, GenAI, and MLOps. Track Record: Proven experience productionizing models (must have moved models beyond the sandbox/prototype phase). Technical Stack: Required: Expert-level proficiency with MLflow and model deployment and monitoring on Databricks. Required: Experience with Databricks Spark, Lakebase, Vector Search. Required: Experience with at least one agent development framework like Langchain, Langgraph, DSPy etc. Good to have: Hands on experience with Databricks Apps, AI/BI Dashboards, Genie etc. Good to have: Databricks Asset Bundle (DAB), Git, experience with large repo management (GitHub, ADO etc.)
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
Remote, Telangana, India
Working pattern and location restrictions need checking in the full posting.
- Work authorization
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