AI Engineer

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Commvault (NASDAQ: CVLT) is the gold standard in cyber resilience. The company empowers customers to uncover, take action, and rapidly recover from cyberattacks – keeping data safe and businesses resilient. The company’s unique AI-powered platform combines best-in-class data protection, exceptional data security, advanced data intelligence, and lightning-fast recovery across any workload or cloud at the lowest TCO. For over 25 years, more than 100,000 organizations and a vast partner ecosystem have relied on Commvault to reduce risks, improve governance, and do more with data. 

AI & Automation Engineer - Marketing
A software engineer first - who builds the automation pipelines and AI agents that run inside Marketing's workflows. You make AI reliable in the real world: prototype fast, harden for production, own it end to end.

01 THE ROLE
You're a software engineer first - clean code, solid systems, reliability that holds in production. On top of that, 
you bring AI into Marketing's day-to-day: automation pipelines that turn campaign, content, and lead data 
into action, and AI agents that reason, use tools, and take action inside marketing workflows. You rapidly 
prototype to prove what works, then harden it for production.
You own the full stack: API design, cloud infra, AI integration, business rules, evaluation, monitoring, and the 
feedback loops that make systems smarter. Marketing is the domain you serve - you don't need to come in 
fluent in it, but you should be curious enough to learn how campaigns, content, and lead scoring actually 
work.

02 WHAT YOU'LL OWN
▪ Re-engineer the process before automating it - map the marketing workflow, cut the steps that 
shouldn't exist, then design the automation around the improved process, not the old one.
▪ Build end-to-end automation - connect marketing inputs (leads, campaign data, content requests, 
forms) to outputs like scored leads, personalized content, and routed campaigns, through an AI 
processing layer.
▪ Design and build AI agent systems - from single-agent task executors (e.g. drafting campaign copy, 
triaging inbound leads) to multi-agent pipelines where agents specialise, coordinate, and hand off.
▪ Design human-in-the-loop governance and guardrails - confidence thresholds, approval gates, 
escalation paths, and hard limits on what an agent may do unsupervised, including brand and 
compliance-sensitive content.
▪ Run AI operations - monitor for drift and hallucination, manage prompt and model versions, control 
token cost, and catch quality regressions before marketing users do.
▪ Integrate Azure OpenAI and other LLM services into production - prompt design, context management, 
response validation, tool use, and function calling.
▪ Build RAG pipelines - ingestion, chunking strategy, embedding, vector store management, and retrieval 
tuning over marketing content and knowledge assets.
▪ Build and run the REST APIs and Azure services behind every pipeline, integrate marketing and CRM 
systems via their APIs and standard protocols like MCP, and write the business-rule and validation layers 
on top of AI output.
▪ Tie every system to a business metric - pipeline contribution, campaign performance, or content velocity 
- instrument it, and report the impact you actually delivered.

03 WHAT YOU BRING
▪ Strong fundamentals - algorithms, data structures, system design. Tested thoroughly in every round.
▪ Clean, maintainable, well-tested code a teammate can extend six months later.
▪ Shipped projects end-to-end - design, build, test, deploy, monitor, maintain.
▪ Comfort with ambiguity - incomplete requirements, surprising systems, figure-it-out energy.
▪ Clear communication of technical decisions to non-technical stakeholders - marketing will be your 
primary partner.
▪ A real track record of self-teaching - our tools will be different in two years.
▪ Intellectual honesty about AI limits - you say clearly what AI can and can't reliably do, and design 
accordingly.
▪ Patience with non-determinism - AI fails in ways hard to reproduce; you work it systematically instead of 
shipping broken things.
▪ Marketing literacy - you know what a funnel, a campaign, an MQL/SQL, and a lead score are. You know 
enough so that a marketing SME doesn't have to explain the domain from scratch. 

04 THE STACK
Core Engineering · Must have
Any OOP language (Python, C# or Java), REST API design, async programming, Azure Functions / App Service 
(or GCP / AWS equivalent), Service Bus, SQL & data modelling, Git · CI/CD, testing
AI & Agents · Must have
Azure OpenAI (or equivalent) / LLM APIs, prompt engineering, classification & extraction, output validation, 
LangChain / LangGraph, Semantic Kernel, tool use / function calling, RAG implementation, MCP servers & 
tools, agent evaluation
AI Operations & Governance · Must have
Human-in-the-loop design, agent guardrails & autonomy limits, drift & hallucination monitoring, prompt / 
model version management, token cost control, eval frameworks & regression tests, outcome 
instrumentation
Bonus · Nice to have
Exposure to marketing or CRM platforms (Salesforce, Dynamics 365, marketing automation tools), vector DB 
internals, LLM observability, fine-tuning, TypeScript / Node.js - none required, all a plus.

05 HOW WE HIRE
▪ 01 · HR - Screen: Communication, culture fit, work ethic.
▪ 02 · HackerRank Test. 85% is the cutoff percentage.
▪ 03 · Tech - Build with us: System design + live coding across automation & agents. Video on, computer & 
solid internet required.
▪ 04 · HR - Final round: Offer & wrap-up.

LOCATION - Hyderabad / Bengaluru
HOURS - 12:00–9:00 PM IST
EXPERIENCE - 2–4 years
TEAM - Marketing Technology

Show us something you've shipped.
A GitHub repo, a deployed system, or a detailed write-up of a real project beats any certification. If clean 
code and hard problems are your thing - you belong here.