Data Scientist at Virtusa
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Jun 28, 2026
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Jul 20, 2026
What this job asks for AI summary
A mid-level role focused on building and shipping generative AI and large language model applications — including retrieval-augmented generation systems, chatbots, and summarization tools — in production environments. The work spans the full ML lifecycle: prompt engineering, fine-tuning, pipeline construction, deployment, and ongoing monitoring. Suited to someone with hands-on applied ML and MLOps experience who can bridge research and engineering.
Mid level · 3+ years · Pune, MH · Full-time
“or” means any one of them counts — you don't need all of them.
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
Rare in this occupation — lead with these, and say what you built with them.
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $120,230 (middle half $85,660–$158,880).
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 28, 2026. It is a model, not a headcount.
Why we read it this way (7)
This role is based in Pune, India (IN-MH-Pune) — US compensation benchmarks do not apply.
The title 'Data Scientist' carries no explicit seniority level; the 3–6 year experience range and scope of responsibilities map to a Mid-level band.
The role has a strong MLOps/engineering flavour (model deployment, CI/CD, microservices), making 15-1252 Software Developers a plausible runner-up, but the core framing — ML algorithms, statistics, evaluation, GenAI/LLM research — aligns more closely with 15-2051 Data Scientists.
The degree requirement states 'Bachelor's or Master's' but explicitly lists both as options with no minimum stated; since either level is acceptable and no hard floor is set, degree requirement is treated as unspecified (None).
MLOps tools (GitHub Actions, Azure ML, SageMaker; MLflow/Weights & Biases; Airflow/Spark) appear under a required 'MLOps & Engineering Skills' section with 'Exposure to' / 'Experience in' / 'Knowledge of' qualifiers — these are treated as hard gates per section placement, though the softer language introduces some ambiguity.
Fine-tuning techniques (LoRA, PEFT) and multi-modal models are listed under 'Preferred Qualifications'.
This role's work location reads as outside the US — the candidate pool, comp, and contention benchmarks here are US-only (BLS employment, Adzuna/USAJOBS demand, and certified H-1B wages), so treat them as a rough US reference, not a local market.
Read the full posting
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