MLOps Engineer at Virtusa
—
Jun 28, 2026
—
Jul 20, 2026
What this job asks for AI summary
An offshore, Hyderabad-based engineering role focused on building and running the end-to-end operational infrastructure for enterprise AI and machine learning workloads. Day-to-day work covers pipeline construction, model and prompt deployment, monitoring, drift detection, and retraining, alongside CI/CD, Terraform-based infrastructure, and integration with messaging systems and API gateways. Suits engineers with a solid MLOps or platform background who have hands-on production experience with LLM patterns such as RAG and vector stores.
Mid level · 4+ years · Hyderabad, India · 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 $135,980 (middle half $105,210–$171,980).
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 (9)
This is an offshore role based in Hyderabad, India; US-based benchmarks do not apply.
SOC classification is Medium confidence: the role is primarily about building and operating MLOps pipelines (software engineering / platform engineering), but it has a meaningful data-science-adjacent dimension (LLM operationalisation, RAG, embeddings, drift monitoring) that could support 15-2051.
Seniority is assessed as Mid (4+ years required) despite the breadth of the role; the posting carries no seniority modifier in the title and the experience gate is consistent with a mid-level individual contributor.
GitHub Actions and the observability tools (Application Insights, Dynatrace) appear in the responsibilities narrative rather than under a dedicated requirements heading; they are listed as preferred accordingly.
Azure API Management appears in both the responsibilities narrative and the Preferred Skills section; it is treated as preferred.
Kafka/Confluent and Azure Service Bus are listed as interchangeable messaging options; one skill is emitted with the other as an alternative. Similarly, MLflow and Azure ML are listed as equivalent ML lifecycle tools.
No compensation was disclosed in the posting.
Ignored 1 non-technology phrase(s) as skills (responsibilities/concepts, not named tools): Azure API Management.
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
The employer publishes the full description on their own site — read it there ↗. Or sign in to read it here — it's free, and it also lets you track this application.