EPAM - AI Architect - Large Language Models

About the Role :

We are looking for a highly experienced AI Architect to lead the architecture, design, and implementation of enterprise-scale AI and intelligent technology solutions. The ideal candidate will have extensive experience in software architecture, artificial intelligence, machine learning, Generative AI, Large Language Models, cloud platforms, data engineering, and distributed systems.

Key Responsibilities :

- Define and own the architecture vision and technology roadmap for AI and Generative AI solutions across the organization.

- Design scalable, secure, reliable, and production-ready AI platforms and solutions capable of supporting enterprise workloads.

- Lead the architecture and implementation of AI/ML solutions across areas such as predictive analytics, intelligent automation, NLP, recommendation systems, computer vision, and Generative AI.

- Design enterprise Generative AI architectures using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, prompt engineering, AI agents, and knowledge systems.

- Evaluate and select appropriate AI models, frameworks, platforms, and technology components based on business requirements, scalability, performance, cost, and security.

- Define architecture patterns for integrating AI capabilities with existing enterprise applications, APIs, data platforms, and digital products.

- Design AI-powered applications using Python and modern AI/ML frameworks.

- Architect data pipelines and data platforms required for training, fine-tuning, evaluation, and serving AI/ML models.

- Establish approaches for model lifecycle management, model deployment, monitoring, observability, versioning, and performance optimization.

- Drive the development of AI agents and multi-agent systems for complex enterprise workflows where appropriate.

- Define strategies for LLM evaluation, hallucination reduction, response quality, grounding, model selection, and continuous improvement.

- Establish responsible AI practices covering security, privacy, governance, explainability, bias mitigation, and regulatory compliance.

- Design secure AI architectures covering data protection, access control, model security, API security, and protection against AI-specific threats.

- Work with engineering teams to establish reusable AI services, frameworks, APIs, and architectural patterns.

- Guide engineering teams in implementing scalable microservices, distributed systems, APIs, event-driven architectures, and cloud-native solutions.

- Provide technical leadership and mentorship to senior engineers, architects, data scientists, and AI/ML teams.

- Review technical designs, architecture proposals, proof-of-concepts, and production implementations.

- Collaborate with senior business and technology stakeholders to identify opportunities where AI can create measurable business value.

- Lead technology evaluations, architecture assessments, and proof-of-concept initiatives for emerging AI technologies.

- Define standards, best practices, coding guidelines, architecture principles, and engineering methodologies for AI development.

- Drive modernization of existing applications by identifying opportunities to embed AI and intelligent automation.

- Monitor emerging developments in Generative AI, foundation models, agentic AI, machine learning, cloud AI services, and AI infrastructure.

- Ensure AI solutions meet enterprise requirements for scalability, availability, performance, security, maintainability, and cost efficiency.

Required Technical Expertise :

- Extensive experience in software architecture and enterprise technology environments.

- Strong expertise in Artificial Intelligence, Machine Learning, and Generative AI.

- Deep understanding of Large Language Models, foundation models, embeddings, transformers, prompt engineering, and model inference.

- Strong hands-on understanding of Retrieval-Augmented Generation (RAG), vector databases, semantic search, knowledge bases, and AI application architectures.

- Experience designing AI agents, agentic workflows, and multi-agent architectures.

- Strong programming experience in Python and familiarity with modern AI/ML libraries and frameworks.

- Strong understanding of ML lifecycle, including model development, training, fine-tuning, evaluation, deployment, monitoring, and optimization.

- Experience with cloud-based AI platforms and services across one or more major cloud environments.

- Strong understanding of distributed systems, microservices, APIs, event-driven architectures, and cloud-native application design.

- Experience with databases, data platforms, data pipelines, and large-scale data processing.

- Understanding of MLOps, DevOps, CI/CD, containerization, orchestration, and infrastructure automation.

- Experience with technologies such as Docker, Kubernetes, Git, and CI/CD platforms.

- Strong understanding of AI security, data privacy, model governance, and responsible AI principles.

Architecture & Leadership:

- Ability to define enterprise-wide AI architecture and technology strategies.

- Strong experience working with senior technology and business leadership.

- Ability to translate complex business problems into practical AI and technology solutions.

- Strong technical decision-making and architecture governance capabilities.

- Experience leading large, cross-functional engineering and technology teams.

- Ability to communicate complex AI concepts clearly to technical and non-technical stakeholders.

- Strong mentoring and technical leadership capabilities.

- Ability to balance innovation with scalability, security, operational stability, and business value.

Preferred Experience :

- Experience building enterprise-wide Generative AI platforms or AI transformation programs.

- Experience with LLM providers and open-source foundation models.

- Experience with vector databases and knowledge retrieval platforms.

- Exposure to AI observability, model monitoring, LLM evaluation, and AI governance platforms.

- Experience implementing AI solutions in highly regulated or security-sensitive environments.

- Experience with enterprise API management, integration platforms, and digital ecosystems.

- Exposure to FinOps and optimization of AI infrastructure and model-serving costs.

- Experience driving organization-wide AI adoption and establishing AI Centers of Excellence.

Key Competencies :

- Strategic technology leadership

- Enterprise architecture

- Artificial Intelligence and Machine Learning

- Generative AI and LLMs

- Agentic AI and AI orchestration

- Cloud and distributed systems

- Data and AI platform architecture

- Security and responsible AI

- Innovation and technology evaluation

- Stakeholder management

- Problem-solving and decision-making

- Team leadership and mentoring