3 days ago
Bengaluru, IndiaStaff+
Responsibilities
- Shape enterprise AI strategy and convert business priorities into phased technical roadmaps.
- Lead AI architecture assessments, target-state definitions, gap analyses, platform selection, modernization planning, and implementation sequencing.
- Define Snowflake-native AI architectures using Cortex, Snowpark, Streamlit, RAG, document intelligence, analytics, governance, security, and cost controls.
- Architect multi-agent systems, AI gateways, control planes, agent registries, context layers, vector search, semantic retrieval, memory, and tool-calling patterns.
- Establish standards for security, privacy, authorization, observability, performance, scalability, reliability, evaluation, auditability, and production support.
- Drive architecture decisions for inference, model routing, model adaptation and fine-tuning, caching, cost optimization, and deployment platforms.
- Produce authoritative blueprints, architecture decision records, sequence diagrams, design specifications, integration patterns, reference architectures, and governance playbooks.
- Lead and integrate the work of domain architects and subject-matter specialists while communicating decisions to executive and delivery stakeholders.
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 12 years of experience in software engineering, data engineering, AI/ML engineering, cloud architecture, or enterprise technology architecture.
- At least 8 years designing and deploying enterprise-grade advanced AI, data, analytics, or cloud-native solutions using at least one 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 patterns.
- At least 2 years architecting and operationalizing LLM-driven application patterns in production or enterprise-scale environments.
- At least 6 years of experience in engineering, machine learning, deep learning, NLP, data engineering, or large-scale analytical engineering applications.
- Demonstrated senior-architect experience aligning AI technology choices with business, risk, regulatory, security, and compliance expectations in industry domains such as banking, insurance, retail, healthcare, travel, logistics, or telecom.
- Deep experience with Snowflake Cortex AI, Cortex Agents, Cortex Search, Cortex Analyst, Cortex AI Functions, Snowpark, Streamlit in Snowflake, Dynamic Tables, Tasks, Streams, Snowflake ML, RBAC, masking policies, access history, lineage, and observability.
- Experience with RAG, embeddings, vector databases, semantic retrieval, context engineering, multi-agent orchestration, tool calling, memory, model routing, GenAI evaluation, AI governance, and production controls.
- Preferred qualifications include SnowPro Advanced Architect or SnowPro Advanced Data Engineer certification and exposure to Snowpark Python, semantic models, dbt, Native Apps, data sharing, Cortex Guardrails, open-source AI frameworks, responsible AI, model risk management, red-teaming, synthetic data, human-in-the-loop review, A/B testing, and GenAI FinOps.
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.