Über diese Large Language Model Architect Stelle bei Accenture
Accenture · Vor Ort · Pune
Project Role : Large Language Model Architect
Project Role Description : Architect large language models (LLM) that can process and generate natural language. Design neural network parameters, trained on large quantities of unlabeled text data.
Must have skills : Large Language Models (LLMs)
Good to have skills : Databricks Unified Data Analytics Platform
Minimum 15 year(s) of experience is required
Educational Qualification : 15 years full time education
Role Summary / Description
AI Powered Tech Talent
- Lead / Senior Architect in AI LLM Technology Architecture, serving as the definitive technical authority for end-to-end AI architecture on Databricks.
- Own the complete AI platform architecture spanning classical machine learning, generative AI, LLM applications, RAG, agentic systems, context engineering, model platforms, inference and enterprise AI integration.
- Operate at executive and senior stakeholder level, connecting business goals, industry priorities and transformation agendas to a coherent technical vision and sequenced AI implementation roadmap.
- Bring practical industry experience in financial services, healthcare, manufacturing, retail, telecom or life sciences to ensure AI architectures address domain realities, regulatory expectations, security constraints, operational processes and measurable business outcomes.
- Lead and integrate work across multiple domain architects and subject matter specialists, resolving cross-domain design decisions and ensuring the full AI solution is cohesive, technically sound and enterprise-ready.
Key Responsibilities
- Partner with CIOs, CTOs, business leaders and delivery stakeholders to shape enterprise AI strategy and convert business priorities into phased technical roadmaps.
- Lead enterprise AI architecture assessments, target-state definition, gap analysis, platform selection, modernization opportunities and implementation sequencing.
- For Databricks, set the enterprise direction for lakehouse-native AI platforms define governed RAG and multi-agent architectures using Delta Lake, Unity Catalog, Vector Search and Mosaic AI establish model serving, evaluation, tracing and monitoring patterns through MLflow and Databricks workflows guide architecture decisions for data governance, scalability and cost control.
- Own the end-to-end technical solution for complex AI platforms, ensuring all domains are aligned to business objectives, enterprise standards and non-functional requirements.
- Define architectural direction for model- and tool-agnostic multi-agent systems including orchestration, memory, tool/skill use, agent registry, AI gateway/control-plane patterns and service abstraction.
- Establish the enterprise context layer architecture spanning knowledge graphs, ontologies, vector search, semantic retrieval, prompt/context assembly and conversation state management.
- Set security, governance, observability, performance, scalability and reliability standards across AI solution domains, including identity, authorization, PII protection, layered guardrails, auditability and evaluation gates.
- Mandate productized evaluation and observability practices covering accuracy, relevance, groundedness, latency, model quality, cost, safety, reliability and production support metrics.
- Drive architecture decisions for high-throughput, low-latency inference, model routing, model adaptation/fine-tuning, caching, cost controls and production-ready deployment platforms.
- Produce and own authoritative architecture artifacts including blueprints, ADRs, sequence diagrams, design specifications, integration patterns, reusable reference architectures and governance playbooks.
Required Qualifications
- Bachelor's degree or equivalent in Computer Science, Computer Engineering, Data Science, AI/ML, Information Technology or a related engineering discipline.
- Minimum 12+ years of overall experience in software engineering, data engineering, AI/ML engineering, cloud architecture or enterprise technology architecture.
- Minimum 8+ years of experience designing and deploying enterprise-grade advanced AI, data, analytics or cloud-native solutions using at least one cloud vendor.
- Minimum 2+ years of experience in LLM and generative AI solution architecture, including agentic AI, RAG, prompt engineering, model integration and evaluation patterns.
- Minimum 2+ years of experience architecting and operationalizing LLM-driven application architecture patterns in production or enterprise-scale environments.
- Minimum 6+ years of experience in engineering, machine learning, deep learning, NLP solutions, data engineering or large-scale analytical engineering applications.
- Demonstrated experience as a senior architect in industry domains such as financial services, healthcare, manufacturing, retail, telecom or life sciences, with ability to align technology choices to business, risk and compliance expectations.
Required Skills/ Experience
- Deep architecture and hands-on engineering experience with Databricks Mosaic AI, Model Serving, Agent Framework, MLflow tracing/evaluation, Vector Search, Unity Catalog, Delta Lake, Delta Live Tables, Lakehouse Monitoring, Feature Store, Databricks Workflows, Jobs and Model Training.
- Strong ability to shape enterprise-grade AI architecture covering RAG, embeddings, vector databases, semantic retrieval, context engineering, multi-agent orchestration, tool calling, memory, model routing and GenAI evaluation.
- Experience defining NFRs 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, making evidence-based technology recommendations and defensible architecture trade-offs.
- Experience establishing AI gateway/control-plane patterns, agent registry and certification gates, authorization models, layered guardrails, model risk controls and production governance.
- Strong stakeholder management and thought leadership skills with ability to communicate architecture decisions to executives, product leaders, security teams and engineering delivery teams.
Good to Have Skills
- Databricks Machine Learning Professional, Data Engineer Professional or Generative AI certification exposure to Spark/PySpark, Unity Catalog governance, Delta Live Tables, MLflow, LangGraph/LangChain, model fine-tuning and lakehouse performance optimization.
- Exposure to open-source AI and orchestration frameworks such as LangChain, LangGraph, LlamaIndex, Haystack, Semantic Kernel, MLflow, FastAPI, Docker and Kubernetes.
- Experience with responsible AI, model risk management, AI governance boards, red-teaming, synthetic data, human-in-the-loop review, A/B testing and GenAI FinOps.
- Recognized thought leadership through reusable architecture assets, platform accelerators, whitepapers, client advisory, internal capability building or conference/community participation.
15 years full time education
Project Role Description : Architect large language models (LLM) that can process and generate natural language. Design neural network parameters, trained on large quantities of unlabeled text data.
Must have skills : Large Language Models (LLMs)
Good to have skills : Databricks Unified Data Analytics Platform
Minimum 15 year(s) of experience is required
Educational Qualification : 15 years full time education
Role Summary / Description
AI Powered Tech Talent
- Lead / Senior Architect in AI LLM Technology Architecture, serving as the definitive technical authority for end-to-end AI architecture on Databricks.
- Own the complete AI platform architecture spanning classical machine learning, generative AI, LLM applications, RAG, agentic systems, context engineering, model platforms, inference and enterprise AI integration.
- Operate at executive and senior stakeholder level, connecting business goals, industry priorities and transformation agendas to a coherent technical vision and sequenced AI implementation roadmap.
- Bring practical industry experience in financial services, healthcare, manufacturing, retail, telecom or life sciences to ensure AI architectures address domain realities, regulatory expectations, security constraints, operational processes and measurable business outcomes.
- Lead and integrate work across multiple domain architects and subject matter specialists, resolving cross-domain design decisions and ensuring the full AI solution is cohesive, technically sound and enterprise-ready.
Key Responsibilities
- Partner with CIOs, CTOs, business leaders and delivery stakeholders to shape enterprise AI strategy and convert business priorities into phased technical roadmaps.
- Lead enterprise AI architecture assessments, target-state definition, gap analysis, platform selection, modernization opportunities and implementation sequencing.
- For Databricks, set the enterprise direction for lakehouse-native AI platforms define governed RAG and multi-agent architectures using Delta Lake, Unity Catalog, Vector Search and Mosaic AI establish model serving, evaluation, tracing and monitoring patterns through MLflow and Databricks workflows guide architecture decisions for data governance, scalability and cost control.
- Own the end-to-end technical solution for complex AI platforms, ensuring all domains are aligned to business objectives, enterprise standards and non-functional requirements.
- Define architectural direction for model- and tool-agnostic multi-agent systems including orchestration, memory, tool/skill use, agent registry, AI gateway/control-plane patterns and service abstraction.
- Establish the enterprise context layer architecture spanning knowledge graphs, ontologies, vector search, semantic retrieval, prompt/context assembly and conversation state management.
- Set security, governance, observability, performance, scalability and reliability standards across AI solution domains, including identity, authorization, PII protection, layered guardrails, auditability and evaluation gates.
- Mandate productized evaluation and observability practices covering accuracy, relevance, groundedness, latency, model quality, cost, safety, reliability and production support metrics.
- Drive architecture decisions for high-throughput, low-latency inference, model routing, model adaptation/fine-tuning, caching, cost controls and production-ready deployment platforms.
- Produce and own authoritative architecture artifacts including blueprints, ADRs, sequence diagrams, design specifications, integration patterns, reusable reference architectures and governance playbooks.
Required Qualifications
- Bachelor's degree or equivalent in Computer Science, Computer Engineering, Data Science, AI/ML, Information Technology or a related engineering discipline.
- Minimum 12+ years of overall experience in software engineering, data engineering, AI/ML engineering, cloud architecture or enterprise technology architecture.
- Minimum 8+ years of experience designing and deploying enterprise-grade advanced AI, data, analytics or cloud-native solutions using at least one cloud vendor.
- Minimum 2+ years of experience in LLM and generative AI solution architecture, including agentic AI, RAG, prompt engineering, model integration and evaluation patterns.
- Minimum 2+ years of experience architecting and operationalizing LLM-driven application architecture patterns in production or enterprise-scale environments.
- Minimum 6+ years of experience in engineering, machine learning, deep learning, NLP solutions, data engineering or large-scale analytical engineering applications.
- Demonstrated experience as a senior architect in industry domains such as financial services, healthcare, manufacturing, retail, telecom or life sciences, with ability to align technology choices to business, risk and compliance expectations.
Required Skills/ Experience
- Deep architecture and hands-on engineering experience with Databricks Mosaic AI, Model Serving, Agent Framework, MLflow tracing/evaluation, Vector Search, Unity Catalog, Delta Lake, Delta Live Tables, Lakehouse Monitoring, Feature Store, Databricks Workflows, Jobs and Model Training.
- Strong ability to shape enterprise-grade AI architecture covering RAG, embeddings, vector databases, semantic retrieval, context engineering, multi-agent orchestration, tool calling, memory, model routing and GenAI evaluation.
- Experience defining NFRs 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, making evidence-based technology recommendations and defensible architecture trade-offs.
- Experience establishing AI gateway/control-plane patterns, agent registry and certification gates, authorization models, layered guardrails, model risk controls and production governance.
- Strong stakeholder management and thought leadership skills with ability to communicate architecture decisions to executives, product leaders, security teams and engineering delivery teams.
Good to Have Skills
- Databricks Machine Learning Professional, Data Engineer Professional or Generative AI certification exposure to Spark/PySpark, Unity Catalog governance, Delta Live Tables, MLflow, LangGraph/LangChain, model fine-tuning and lakehouse performance optimization.
- Exposure to open-source AI and orchestration frameworks such as LangChain, LangGraph, LlamaIndex, Haystack, Semantic Kernel, MLflow, FastAPI, Docker and Kubernetes.
- Experience with responsible AI, model risk management, AI governance boards, red-teaming, synthetic data, human-in-the-loop review, A/B testing and GenAI FinOps.
- Recognized thought leadership through reusable architecture assets, platform accelerators, whitepapers, client advisory, internal capability building or conference/community participation.
15 years full time education
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