About Kearney PERLab

Kearney PERLab (Product Excellence and Renewal Lab) is Kearneys global practice for “everything product.” We help companies that design, source, formulate, manufacture, and improve physical products make better decisions across the full product lifecycle - from new product development and product formulation to teardown and benchmarking, should costing, BOM optimization, supplier strategy, raw material and commodity choices, supply risk, manufacturing improvement, and sustainability.

Our work combines strategy, product engineering, design, advanced analytics, and AI to help clients move faster, see deeper across larger and more diverse data sets, and create better products at lower complexity and cost.

Our flagship platform, Velocity AI, is a PERLab accelerator powered by agentic AI. Velocity AI connects domain workflows, proprietary data assets, and intelligent agents to accelerate product decisions, create digital concepts of products, and support client discussions with working prototypes and deployable tools.

What makes this role different

- Azure DevOps comes first: you own the Azure estate behind every Velocity AI module.
- When the platform is stable, you build data pipelines and Velocity AI features.
- You own uptime, access, and cost, and switch easily between infrastructure and code.

About the Role As Azure DevOps & AI Full-Stack Engineer, you own the Azure footprint behind Velocity AI, keeping subscriptions, environments, pipelines, and AI services secure and repeatable.

Your primary focus is Azure DevOps and platform operations: CI/CD, infrastructure-as-code, Azure AI Foundry, identity and access, monitoring, and cost across all Velocity AI modules.

When platform work is light, you move into data and application engineering: ingestion pipelines, knowledge graphs, and Velocity AI features. Expect roughly 60 to 70 percent of your time on platform work.

Key Responsibilities

1. Own Azure DevOps for the Velocity AI platform

- Own repos, branch policies, YAML pipelines, environments, and service connections across all modules.
- Run gated promotion from dev through QA, UAT, and production with clear rollback paths.
- Standardise pipeline templates so new modules and services can be onboarded quickly and consistently.

2. Manage Velocity Azure resources as code

- Provision Azure resources with Bicep or Terraform across App Service, AKS, Functions, databases, and Key Vault.
- Keep subscriptions, resource groups, naming, and tagging organised so ownership and cost are traceable.
- Track and optimise Azure spend, including AI model and token usage, and report on it regularly.

3. Operate Azure AI Foundry and AI services

- Manage Azure AI Foundry projects, model deployments, quotas, and capacity for Azure OpenAI.
- Operate Azure AI Search indexes,



agent and prompt flow deployments, and content safety settings.
- Monitor model latency, availability, and consumption, and plan capacity ahead of client pilots and demos.

4. Build data engineering and knowledge foundations

- Build ingestion pipelines for documents, BOMs, and supplier data with Data Factory, Databricks, or Fabric.
- Process large datasets with Spark and Delta Lake, with incremental loads, quality checks, and lineage.
- Build knowledge graphs linking products, parts, materials, suppliers, and costs for agents and retrieval.

5. Maintain security, observability, and operability

- Implement Entra ID, RBAC, managed identities, Key Vault, private endpoints, and VNet integration.
- Set up monitoring with Azure Monitor, Application Insights, and Log Analytics, and own incident triage.
- Maintain runbooks and architecture records, and partner with Kearney security and client IT teams.

6. Build Velocity AI applications when platform work allows

- Ship features across Velocity AI with React, Next.js, and TypeScript, plus Python (FastAPI) or Node.js.
- Integrate LLM capabilities: retrieval pipelines, agent workflows, tool calling, and structured outputs.
- Debug end to end, from UI to API to data to model, and keep app code deployable through your pipelines.

What Great Looks Like

- Strong hands-on Azure DevOps: multi-stage YAML pipelines, environments, approvals, and release management.
- Production infrastructure-as-code on Azure with Bicep or Terraform.
- Practical experience with Azure OpenAI or Azure AI Foundry: deployments, quotas, networking, and access.
- Experience with containers and Azure hosting: Docker plus AKS, Container Apps, or App Service.
- Good security instincts across Entra ID, RBAC, managed identities, Key Vault, and private networking.
- Hands-on ingestion pipelines with Azure Data Factory, Databricks, Microsoft Fabric, or Synapse.
- Big data processing with Spark, Delta Lake, and SQL, including data modelling and quality controls.
- Knowledge graph design and querying with Neo4j (Cypher), Cosmos DB Gremlin, or RDF-based stores.
- Current hands-on coding in TypeScript and React, plus Python or Node.js for backend services.
- Strong scripting and observability skills with PowerShell, Bash, Azure CLI, and KQL in Log Analytics.

Client-Facing Mindset

- Communicate clearly with senior client stakeholders, domain experts, and non-technical audiences.




- Translate messy business problems into crisp technical or product decisions.
- Understand where AI can add value in real product and operations workflows rather than chasing novelty.

Personal Traits

- High agency and ownership: you move from ambiguity to action without waiting for perfect instructions.
- Speed with judgment: you move fast, but you know where rigor matters.
- Curiosity and range: you enjoy learning new tools, domains, and problem spaces.
- Low ego, high collaboration: you work well with partners, clients, and specialists across disciplines.
- Responsible use of AI: you care about security, reliability, and practical value, not just impressive demos.

Nice to Have

- Azure certifications such as AZ-400, AZ-104, or AI-102.
- Experience with Azure AI Search, prompt flow, Semantic Kernel, LangChain, or similar AI orchestration tools.
- Experience with GitHub Actions alongside Azure DevOps.
- FinOps and cost optimization experience for AI-heavy workloads on Azure.
- Experience combining knowledge graphs with vector search for GraphRAG or hybrid retrieval.
- Exposure to Event Hubs, Kafka, or other streaming platforms.

Why Work Here Be part of a small, senior, high-impact team that sits at the intersection of management consulting, product engineering, and AI innovation. At PERLab, you will build prototypes and products that directly influence how leading companies make critical product and cost decisions. You will work on real client problems across industries, partner closely with experienced domain experts, and operate in an workplace that rewards ownership, craft, curiosity, and the ability to turn ambiguity into working solutions.

Equal Employment Opportunity and Non-discrimination

Kearney prides itself on providing a culture that allows employees to bring their best selves to work every day. Our people can feel comfortable, confident, and joyful to do great things for our firm, our teams, and our clients. Kearney aims to build diverse capabilities to help our clients solve their most mission critical problems.

Kearney is committed to building a diverse, unbiased, and inclusive workforce. Kearney is an equal opportunity employer; we recruit, hire, train, promote, develop, and provide other conditions of employment without regard to a person’s gender identity or expression, sexual orientation, race, religion, age, national origin, disability, marital status, pregnancy status, veteran status, genetic information, or any other differences consistent with applicable laws. This includes providing reasonable accommodation for disabilities or religious beliefs and practices.

Members of communities historically underrepresented in technology and consulting are encouraged to apply.

📌 Senior Azure Devops and AI Full Stack Engineer (Bengaluru)
🏢 Kearney
📍 Bengaluru