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 : Machine Learning (ML)
Good to have skills : NA
Minimum 7.5 year(s) of experience is required
Educational Qualification : 15 years full time education

Key Skills :
Complex Program Management AI Transformation Delivery AI-Native Development Practices Complex Software Engineering Understanding Technical Architecture Literacy AI/ML Systems Understanding Platform Engineering Principles Distributed Systems & Microservices Agile & Lean Methodologies Stakeholder Management Risk & Change Management Resource & Budget Management Cross-Functional Team Leadership Business Value Articulation Vendor & Partner Management Organizational Change Management Executive Communication Delivery Metrics & Reporting

Key Responsibilities :
Lead complex, multi-workstream AI transformation programs from initiation through deployment and scaling
Coordinate cross-functional teams including engineers, architects, product managers, and business stakeholders to drive delivery
Establish and manage program governance, risk management, and delivery frameworks tailored to AI-native ways of working
Navigate and guide AI-native development practices where specifications emerge from building, not upfront documentation
Maintain technical fluency to engage credibly with complex software engineering challenges including distributed systems, agentic workflows, semantic technologies, and platform architectures
Assess and communicate technical risks specific to AI systems (model drift, hallucinations, data quality, performance at scale)
Drive stakeholder alignment across senior leadership, ensuring clarity on objectives, progress, risks, and value realization
Navigate organizational complexity and remove blockers to maintain program momentum and team velocity
Define and track delivery metrics, KPIs, and business outcomes to demonstrate transformation impact
Champion agile/lean delivery practices and continuous improvement across transformation teams, adapting traditional methodologies for AI-native contexts
Manage program budgets, resource allocation, and vendor relationships for AI initiatives Partner with architects to ensure delivery approaches support platform engineering principles and reusability across initiatives

Success Characteristics :
Execution Catalyst: Turns vision into reality by orchestrating people, technology, and processes to deliver complex transformations successfully
Strategic Operator: Balances big-picture thinking with tactical execution, ensuring programs stay aligned to business goals while navigating day-to-day complexity
Technical Translator: Bridges technical and business domains with deep understanding of AI complexity, enabling credible engagement with both engineers and executives
Trust Builder: Creates confidence across all levels of the organization through transparent communication, proactive risk management, and consistent delivery

15 years full time education

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