20 hours ago
Pune, IndiaStaff+
Responsibilities
- Partner with CIOs, CTOs, business leaders, and delivery stakeholders to define enterprise AI strategy and phased technical roadmaps.
- Lead AI architecture assessments, target-state definitions, gap analyses, platform selection, modernization planning, and implementation sequencing.
- Set Databricks architecture direction for lakehouse-native AI, governed RAG, multi-agent systems, model serving, evaluation, tracing, monitoring, governance, scalability, and cost control.
- Own end-to-end technical solutions for complex AI platforms and align domain architectures with business objectives, enterprise standards, and non-functional requirements.
- Define architecture for model- and tool-agnostic multi-agent systems, including orchestration, memory, tool use, agent registries, gateways, and control planes.
- Establish context-layer architecture covering knowledge graphs, ontologies, vector search, semantic retrieval, prompt and context assembly, and conversation state.
- Set standards for security, governance, observability, performance, scalability, reliability, identity, authorization, PII protection, guardrails, auditability, and evaluation.
- Drive high-throughput and low-latency inference, model routing, fine-tuning, caching, cost controls, and production deployment decisions.
- Create authoritative blueprints, architecture decision records, sequence diagrams, design specifications, integration patterns, 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 15 years of experience is required.
- At least 12 years of experience in software engineering, data engineering, AI/ML engineering, cloud architecture, or enterprise technology architecture.
- At least 8 years of experience designing and deploying enterprise-grade AI, data, analytics, or cloud-native solutions using a cloud vendor.
- At least 2 years of experience in LLM and generative AI solution architecture, including agentic AI, RAG, prompt engineering, model integration, and evaluation.
- At least 2 years of experience architecting and operationalizing LLM-driven applications in production or enterprise-scale environments.
- At least 6 years of experience in engineering, machine learning, deep learning, NLP solutions, data engineering, or large-scale analytical engineering applications.
- Demonstrated senior-architect experience in financial services, healthcare, manufacturing, retail, telecom, or life sciences, aligning technology choices with business, risk, and compliance needs.
- Deep hands-on architecture experience with Databricks Mosaic AI, Model Serving, Agent Framework, MLflow, Vector Search, Unity Catalog, Delta Lake, Delta Live Tables, Lakehouse Monitoring, Feature Store, Databricks Workflows, Jobs, and Model Training.
- Experience with RAG, embeddings, vector databases, semantic retrieval, context engineering, multi-agent orchestration, tool calling, memory, model routing, and generative-AI evaluation.
- Experience defining non-functional requirements and architecture controls for performance, scalability, security, privacy, governance, observability, resiliency, cost optimization, and operational readiness.
- Ability to compare platforms, frameworks, foundation models, and deployment patterns and make evidence-based architecture recommendations.
- Experience with AI gateway and control-plane patterns, agent registries, certification gates, authorization models, layered guardrails, model-risk controls, and production governance.
- Strong stakeholder management and thought leadership skills for communicating architecture decisions to executives, product leaders, security teams, and engineering delivery teams.
- Preferred qualifications include Databricks Machine Learning Professional, Data Engineer Professional, or Generative AI certification exposure; Spark/PySpark, Unity Catalog governance, Delta Live Tables, MLflow, LangGraph/LangChain, model fine-tuning, and lakehouse performance optimization experience.
- Preferred experience includes open-source AI and orchestration frameworks, responsible AI, model risk management, AI governance boards, red-teaming, synthetic data, human-in-the-loop review, A/B testing, GenAI FinOps, and recognized architecture thought leadership.
Tech Stack
Categories
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.