Technology Architect

Project Role : Technology Architect
Project Role Description : Design and deliver technology architecture for a platform, product, or engagement. Define solutions to meet performance, capability, and scalability needs.
Must have skills : Accenture Delivery Architectures (ADA)
Good to have skills : NA
Minimum 12 year(s) of experience is required
Educational Qualification : 15 years full time education

Role Summary
We are seeking a highly experienced AI Architect with 12+ years of overall experience and deep hands-on expertise across Agentic AI, Generative AI, and Traditional AI/Machine Learning. The role will be responsible for architecting, designing, and governing end-to-end AI solutions—from data and model foundations to agentic workflows and enterprise-scale deployment.
The ideal candidate combines strong AI/ML depth, system-level architectural thinking, and practical delivery experience across complex, real-world use cases.
Key Responsibilities
1. AI & Agentic Architecture
Design end-to-end AI architecture spanning data ingestion, feature engineering, model development, agent orchestration, and application integration
Define Agentic AI architectures, including:
o Multi-agent collaboration patterns
o Tool-using agents and workflow orchestration
o Memory, planning, reasoning, and feedback loops
Select and apply appropriate agentic design patterns (RAG, Graph-RAG, hierarchical agents, event-driven agents, exception-based agents)

2. Generative AI & LLM Systems
Architect Generative AI solutions using LLMs, VLMs, and multimodal models
Design and govern:
o Prompt engineering and prompt management strategies
o RAG pipelines (vector, hybrid, graph-based)
o Embedding pipelines and semantic indexing strategies
Ensure accuracy, explainability, safety, and cost efficiency of GenAI solutions
Define approaches for LLM evaluation, grounding, hallucination mitigation, and guardrails

3. Traditional AI / Machine Learning
Provide architectural oversight for classical ML systems, including:
o Regression, classification, clustering, anomaly detection, and forecasting
o Time-series modeling and predictive analytics
Guide model lifecycle management:
o Feature engineering strategies
o Model selection, validation, and performance metrics
o Model drift, data drift, and retraining strategies

4. Data & Platform Architecture
Design scalable data architectures supporting AI workloads:
o Data lakes / lakehouses
o Feature stores
o Vector databases
o Knowledge graphs
Define data quality, data contextualization, and metadata management strategies
Collaborate with data engineering teams on ingestion, transformation, and governance pipelines

5. MLOps, LLMOps & AI Governance
Architect MLOps / LLMOps frameworks, including:
o CI/CD for models and prompts
o Model versioning and lineage
o Monitoring, observability, and alerting
Define AI governance frameworks covering:
o Security, privacy, and compliance
o Auditability and explainability
o Responsible AI and regulatory alignment

6. Enterprise Integration & Delivery
Integrate AI systems with enterprise IT and OT systems (ERP, historians, MES, CRM, etc.)
Support solution blueprinting, technical design reviews, and architecture sign-offs
Act as a technical authority during PoCs, pilots, and scaled deployments
Mentor engineers, data scientists, and technical team

Required Experience & Skills
Experience
12+ years overall experience in software engineering, data, and AI systems
5+ years hands-on experience with ML/AI solutions in production
3+ years of practical experience with Generative AI and/or Agentic AI systems

Must Have
Strong architectural thinking and abstraction skills
Ability to translate business problems into AI solution designs
Excellent stakeholder communication and technical storytelling
Comfortable working in ambiguous, evolving problem spaces
Proven experience influencing technical direction at scale

Nice-to-Have
Experience with industrial, manufacturing, energy, or enterprise AI use cases
Exposure to optimization, simulation, or decision-support systems
Contributions to internal AI platforms, accelerators, or reusable frameworks

Role Level & Impact
Senior / Principal individual contributor or technical leadership role
High impact on AI strategy, platform evolution, and solution quality
Opportunity to shape next-generation Agentic and Generative AI systems