3 days ago
Bengaluru, IndiaStaff+
Responsibilities
- Define enterprise AI strategy, target-state architecture, investment roadmaps, and implementation sequencing with client executives and business leaders.
- Own end-to-end architecture for advanced AI platforms and solutions spanning machine learning, generative AI, LLM applications, agentic systems, model platforms, inference, AI operations, and enterprise integration.
- Define AWS-native AI platform strategy using Bedrock, SageMaker, open-source frameworks, multi-account patterns, security, observability, resilience, and GenAI FinOps.
- Lead enterprise AI assessments, technology comparisons, platform selection, modernization planning, and reference architecture development.
- Set architectural direction for multi-agent systems, orchestration, memory, tool use, agent registries, AI gateways, control planes, risk scoring, and certification gates.
- Define context-layer architecture covering knowledge graphs, ontologies, vector search, semantic retrieval, prompt and context assembly, conversational state, and reusable memory services.
- Establish identity, authorization, guardrails, prompt-injection defense, PII protection, audit logging, lineage, and defense-in-depth controls.
- Establish evaluation and observability practices for accuracy, relevance, groundedness, model quality, latency, cost, safety, reliability, and continuous improvement.
- Produce and maintain enterprise AI blueprints, architecture decision records, sequence diagrams, solution patterns, interface specifications, reference architectures, and governance playbooks.
Requirements
- Bachelor's degree or equivalent in Computer Science, Computer Engineering, Data Science, AI/ML, Information Technology, or a related engineering discipline.
- Minimum 18 years of overall experience is required.
- At least 15 years of experience across software engineering, data engineering, AI/ML engineering, cloud architecture, enterprise architecture, or technology leadership.
- At least 8 years designing and deploying enterprise-grade AI, data, analytics, or cloud-native solutions using at least one cloud vendor.
- At least 2 years designing LLM and generative AI solutions, including agentic systems, RAG, prompt engineering, model integration, and evaluation patterns.
- At least 2 years architecting and operationalizing LLM-driven applications in enterprise-scale or production environments.
- At least 6 years of experience in engineering, machine learning, deep learning, NLP, data engineering, or large-scale analytical engineering.
- At least 6 years as a machine learning, data, or AI architect designing large-scale analytical engineering solutions in industry contexts.
- Deep experience with Amazon Bedrock, Bedrock Agents or AgentCore, Knowledge Bases, Bedrock Guardrails, Bedrock model evaluation, SageMaker, Lambda, API Gateway, Step Functions, EventBridge, OpenSearch Serverless or Vector Engine, S3, IAM, VPC, KMS, CloudWatch, CloudTrail, and AWS Well-Architected practices.
- Expertise in RAG, embeddings, vector databases, semantic retrieval, context engineering, model routing, agent orchestration, memory, tool calling, AI gateways, and model evaluation.
- Experience establishing architectural controls, agent registries, certification models, AI control planes, access models, production evaluation stacks, model risk controls, and cross-platform governance.
- Preferred qualifications include AWS certifications, AWS CDK or Terraform, EKS, Amazon Q, private connectivity, landing zones, regulated workloads, enterprise FinOps, open-source AI frameworks, responsible AI, model risk management, red-teaming, synthetic data, human-in-the-loop review, A/B testing, and recognized AI architecture thought leadership.
Tech Stack
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About Accenture
Accenture is a global professional services firm providing management consulting, systems integration and technology, cybersecurity, and business process outsourcing for enterprises and governments. It operates a services-driven model delivering projects and managed services, often with major cloud and software partners, across industries worldwide. Headquartered in Dublin and publicly traded on the NYSE (ACN), it originated as Andersen Consulting and adopted the Accenture name in 2001.