Description

Key Responsibilities

• Define the organization’s generative AI architecture, roadmap, and technical standards.
• Design scalable solutions using LLMs, foundation models, RAG, vector databases, prompt engineering, fine-tuning, and AI agents.
• Evaluate and select cloud AI services, open-source models, frameworks, and third-party platforms.
• Develop reference architectures and reusable patterns for GenAI applications.
• Lead the integration of AI solutions with enterprise systems, APIs, data platforms, and business workflows.
• Establish practices for model evaluation, observability, monitoring, security, governance, and cost optimization.
• Design strategies for prompt management, context engineering, model routing, guardrails, and human-in-the-loop workflows.
• Ensure compliance with privacy, security, regulatory, and responsible AI requirements.
• Collaborate with product managers, data scientists, ML engineers, software engineers, security teams, and business stakeholders.
• Create technical documentation, architecture decision records, prototypes, and proof-of-concepts.
• Mentor engineering teams and provide technical leadership throughout the solution lifecycle.
• Stay current with developments in foundation models, multimodal AI, agentic systems, and AI infrastructure.

Required Qualifications

• Bachelor’s or master’s degree in Computer Science, Engineering, Data Science, or a related field.
• 8+ years of experience in software, data, cloud, or solution architecture.
• 3+ years of hands-on experience designing and delivering AI/ML or GenAI solutions.
• Strong understanding of LLMs, embeddings, transformers, tokenization, inference, and model limitations.
• Experience building production systems using RAG, vector search, prompt engineering, and agent frameworks.
• Strong programming experience in Python, Java, JavaScript, or similar languages.
• Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud.
• Knowledge of APIs, microservices, event-driven architecture, containers, Kubernetes, and CI/CD.
• Experience with vector databases and search technologies such as Pinecone, pgvector, Azure AI Search, OpenSearch, or Milvus.
• Familiarity with model evaluation, hallucination mitigation, prompt injection defense, and AI safety techniques

Key Skills

Education

Bachelor's degree