Manager – Applied Intelligence
GenAI Platform Architect (LLMOps & Enterprise GenAI Platform)
Accenture Applied Intelligence practice help our clients grow their business in entirely new ways. Analytics enables our clients to achieve high performance through insights from data - insights that inform better decisions and strengthen customer relationships. From strategy to execution, Accenture works with organizations to develop analytic capabilities - from accessing and reporting on data to predictive modelling - to outperform the competition.
As part of our Analytics practice, you will join a worldwide network of smart and driven colleagues experienced in leading tools, methods and applications. From data to analytics and insights to actions, our forward-thinking consultants provide analytically-informed, issue-based insights at scale to help our clients improve outcomes and achieve high performance.
Manager in Applied Intelligence
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GenAI Platform Architect (LLMOps & Enterprise GenAI Platform) defines and delivers scalable, secure, reusable GenAI platform capabilities that enable multiple AI products (copilots, chatbots, agentic workflows) to be built and operated across the enterprise. They combine deep cloud-native and platform engineering skills with GenAI patterns (RAG, prompt engineering, evaluation, guardrails) and delivery leadership to ensure reliable, governed, and cost-effective GenAI adoption at scale.
Duties and Responsibilities:
- Own the end-to-end GenAI platform roadmap: shared services, reference architecture, scalability, reliability, security, and cost governance.
- Design and implement LLMOps: CI/CD for prompts, retrieval pipelines, and agent workflows; environment promotion, release gates, rollback and versioning.
- Build reusable platform services for:
- Data ingestion & indexing (chunking, embeddings, metadata enrichment)
- Retrieval services (search APIs, reranking patterns, grounding and citations approach)
- Prompt & configuration management (templates, system prompts, routing rules)
- Evaluation framework (golden datasets, regression tests, automated quality scoring, A/B testing)
- Guardrails and safety (PII controls, policy enforcement, safety filters, secure prompt/data handling)
- Establish production-grade observability for GenAI: tracing, latency, token/cost tracking, quality monitoring, audit logs, and SLOs/SLA reporting.
- Define enterprise patterns for model access (multi-model strategy, model gateway, routing), secure secrets management and access controls.
- Ensure resilient deployments via cloud-native architecture: Docker/Kubernetes, microservices, event-driven patterns, caching, and API management.
- Partner with Security/Risk and Enterprise Architecture teams to pass governance and compliance requirements (data residency, encryption, RBAC/ABAC).
- Lead engineering teams and coordinate across pods; drive sprint planning, technical governance, code quality, and platform documentation.
Support pursuits by creating platform accelerators, architecture diagrams, effort estimates, and delivery plans.
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Educational Background/qualifications
- B Tech/M Tech from reputed engineering colleges
- Masters/M Tech in Computer Science
- Master degree in Statistics/Econometrics/ Economics from reputed institute
Requirements:
- 10–12+ years of experience in software/platform engineering and cloud roles, with 3+ years in architecture and delivery leadership.
- Proven experience building and operating enterprise-grade GenAI systems and platform components (RAG services, prompt management, evaluation, guardrails).
- Strong background in cloud solutioning (AWS/Azure/GCP) and cloud-native engineering (microservices, API design).
- Hands-on experience with containerization and orchestration (Docker/Kubernetes/AKS/EKS) and production operations.
- Strong programming foundation in Python/Java; exposure to modern app stacks (FastAPI/React) preferred.
- Experience with MLOps/LLMOps tooling (e.g., MLflow, Airflow, Kubeflow, Seldon, AzureML, SageMaker) and deployment best practices.
- Exposure to data platforms and storage patterns (Databricks, Delta Lake, ADLS, SQL/NoSQL) preferred.
- Strong stakeholder management and ability to translate technical trade-offs into clear decisions.
Soft Skills
- Strategic thinker with a delivery mindset and strong bias for measurable outcomes.
- Clear, concise communicator able to translate technical tradeoffs to business stakeholders.
- Strong people leadership and mentoring; builds high-performing engineering culture.
Accenture is an equal opportunities employer and welcomes applications from all sections of society and does not discriminate on grounds of race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, or any other basis as protected by applicable law.
