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
18 year(s) of experience is required
Educational Qualification : 15 years full time education
Summary:
As a Large Language Model Architect, your day involves designing and structuring advanced language models capable of understanding and generating human-like text. You focus on creating neural network configurations that efficiently process vast amounts of unstructured text data. Your work includes conceptualizing model architectures that enhance natural language comprehension and generation, ensuring the models are scalable and adaptable to various applications. Collaboration with cross-functional teams to align model capabilities with project goals is a key part of your routine, alongside continuous evaluation and refinement of model performance to meet evolving requirements.
Roles & Responsibilities:
- Role Expectations
- Architect large language models that can process and generate natural language.
- Design neural network parameters, trained on large quantities of unlabeled text data.
- Collaborate with data scientists and engineers to integrate language models into broader systems and applications.
- Evaluate model performance and implement improvements to enhance accuracy and efficiency.
- Stay updated with the latest research and advancements in natural language processing and machine learning.
- Develop documentation and guidelines for model deployment and maintenance.
- Mentor junior team members and support their professional growth within the project.
- Lead solution design for complex AI transformation initiatives, translating business requirements into comprehensive technical solutions
- Architect end-to-end solutions spanning AI/ML models, data pipelines, integration layers, user experiences, and operational systems with platform engineering principles
- Design AI-native architectures that account for agentic workflows, semantic technologies, knowledge graphs, and distributed AI systems
- Understand complex software engineering patterns (microservices, event-driven architectures, real-time systems) to design scalable, maintainable solutions
- Conduct discovery workshops with business stakeholders to deeply understand problems, constraints, and success criteria
- Create solution blueprints, architecture diagrams, and technical proposals for executive and technical audiences
- Evaluate build vs. buy decisions, technology selections, and integration approaches for transformation initiatives with deep technical understanding
- Assess AI-specific architectural concerns (latency, token costs, model drift, data quality, observability) in solution design
- Estimate effort, cost, and timelines for proposed solutions with input from engineering and delivery teams
- Partner with architects, engineers, and product teams to refine solutions from concept through implementation
- Define solution success metrics and value realization approaches tied to business outcomes
- Navigate technical debt, legacy systems, and organizational constraints to design pragmatic, implementable solutions
- Design for reusability and platform value, creating solutions that serve multiple use cases and scale across the organization
- Success Characteristics :
Innovation Pioneer: Constantly exploring the frontier of what's possible with AI, bringing insights back to inform strategic decisions and accelerate transformation
Pragmatic Experimenter: Balances cutting-edge exploration with practical business application, ensuring experiments drive real value and learnings while informing platform engineering patterns
Technical Explorer: Deeply understands complex software architectures and AI-native development, discovering how emerging AI capabilities reshape engineering fundamentals
Knowledge Multiplier: Shares insights generously across the organization, building AI literacy and capability through hands-on experimentation and storytelling
Professional & Technical Skills:
- Must To Have Skills: Proficiency in Large Language Models (LLMs).
- Experience in designing and tuning neural network architectures for natural language processing tasks.
- Strong knowledge of deep learning frameworks and tools used for training large-scale models.
- Ability to handle and preprocess large datasets, particularly unlabeled text corpora.
- Familiarity with techniques for optimizing model training and inference efficiency.
- Capability to analyze model outputs and troubleshoot issues related to language generation and understanding.
- Solution Architecture
- Business Requirements Analysis
- Technical Solution Design
- AI-Native Architecture Patterns
- Complex Software Engineering
- Distributed Systems & Microservices
- Platform Engineering Principles
- Semantic Technologies & Knowledge Graphs
- Agentic Workflow Architecture
- Event-Driven Architectures
- Real-Time Systems Design
- Workshop Facilitation
- Stakeholder Engagement
- Technology Evaluation
- Cost & Effort Estimation
- Integration Architecture
- AI/ML Solution Patterns
- Enterprise Architecture
- Documentation & Proposals
- Value Case Development
Additional Information:
- The candidate should have minimum 20+ years of experience in Large Language Models (LLMs).
- This position is based at our Bengaluru office.
- A 15 years full time education is required.
