AI Application Engineer

About us KPMG Delivery Network India (KDNI) is a diverse entity spread across multiple cities in India. We are an important part of the KPMG Delivery Network (KDN), a global organization that supports KPMG member firms in delivering global priority solutions to their clients across a number of different industries. Our team of over 3,000 professionals in India are known for their technical acumen and business insights, that come together to deliver exceptional client service. When you join KDN, you’ll be a part of the wider KPMG global network working alongside some of our profession’s most skilled practitioners on rewarding programs and initiatives that are changing the way business operates, delivering value to the clients of KPMG member firms and driving positive change in the communities we serve. As a KDN India colleague you will be helping to accelerate new ways of working, using cutting-edge technology to create increased value, and working together with member firm colleagues across the globe to drive sustainable growth and help achieve our ambition to be the most trusted and trustworthy professional services firm. Job Discription and Skills Experience - 18yrs - 25 yrs Location - Bangalore Work Mode - Hybrid Notice Period - Immeditate to 30 days only Skill - AI Engineer with Strong experience in coding AI leader will shape the engineering vision, drive cross-functional squads, and ensure that all AI solutions adhere to KPMG’s Trusted AI Governance, Zero-Trust frameworks, and multi-cloud operational standards. Responsibilities: 1. AI Engineering & Architecture Leadership - Lead full lifecycle AI engineering from prototype to MVP to production across KPMG Audit, Tax, and Advisory use cases. - Architect scalable Agentic AI systems integrated with Foundry pipelines, Agent Factory orchestration, and KDO lakehouse connectivity. - Define modular AI agent architecture (stateless/headless/memory-enabled) using LangChain, CrewAI, LlamaIndex, and Azure AI Studio. - Drive model optimization, fine-tuning, and RLHF strategies for both proprietary and open-weight models (GPT, Claude, Llama, Falcon). 2. Platform Ownership – AI Foundry, Agent Factory - Own the engineering roadmap for the KDN Agent Factory, ensuring agent orchestration, safety, and lifecycle governance. - Integrate AI Foundry pipelines with marketplace for Bring-Your-Own-Agent (BYOA) and Managed AI Service capabilities. - Oversee the continuous delivery of secure, portable agents across Azure Stack, AWS Bedrock, and GCP Vertex. - Collaborate with global COEs to embed explainability, observability, and compliance automation into all releases. 3. Data & Integration Leadership - Partner with Data Engineering teams to evolve Data Ocean as the single source of truth for model training and inference data. - Ensure AI-native integrations with SAP, Oracle, Workday, ServiceNow, and Palantir for enterprise automation scenarios. - Oversee graph and vector-based storage layers (Neo4j, Pinecone, Milvus) and their alignment with Knowledge Graphs / Agent Context Memory. 4. Delivery & Governance - Manage multi-disciplinary engineering squads spanning AI, MLOps, DevSecOps, and Cloud Infra. - Enforce AI SDLC, MLOps, and Responsible AI checkpoints in alignment with KPMG Global Risk & Legal. - Establish performance dashboards tracking velocity, agent uptime, and hallucination-free accuracy across environments. - Drive quarterly WOW Demo and Board Showcases with Foundry and Client Advisory leadership. 5. Client-Facing & Internal Transformation - Translate domain workflows (Audit, Tax, Advisory) into agentic pipelines for efficiency, insight, and compliance automation. - Co-create AI-enabled service models for zero-touch audit, policy assurance, digital twins, and finance anomaly detection. - Serve as AI Engineering liaison for client-side pilots, coordinating with Consulting, Risk, and CIO stakeholders. Required Technical Competencies - AI Frameworks: LangChain, CrewAI, AutoGen, Semantic Kernel, HuggingFace, PyTorch, TensorFlow - Data Platforms: Azure Fabric, Databricks, Snowflake, Synapse, Palantir Foundry - Model Management: MLflow, Kubeflow, Arize, OpenTelemetry, Prometheus - Architecture: Microservices, container orchestration (Kubernetes, Ray), event-driven design - Security: Zero-Trust, OAuth2, Confidential AI, Keysafe, SBOM governance - DevOps Stack: GitHub Enterprise, Azure DevOps, Terraform, Helm, CI/CD pipelines Leadership & Behavioral Competencies - Proven track record in leading 50+ member global AI engineering squads across cloud and hybrid infrastructures. - Deep understanding of business, risk, and regulatory context of Big 4 Audit/Tax/Advisory engagements. - Strong executive communication and board reporting skills; experience presenting AI transformation metrics to CXO-level audiences. - Ability to balance innovation velocity with compliance assurance under regulated enterprise environments. Educational & Professional Background - Ph.D. / Master’s in Computer Science, AI/ML, or related discipline. - 12+ years in AI Engineering, including 5+ years in leadership roles. - Experience within professional services, consulting, or enterprise AI transformation