Generative AI (GenAI) Architect

We are seeking a Generative AI (GenAI) Architect to lead the design, development, and deployment of AI-powered solutions using large language models (LLMs), multimodal models, and modern AI infrastructure. This role bridges business strategy, AI engineering, cloud architecture, and responsible AI practices to deliver scalable and secure GenAI applications.

Key Responsibilities:

Design end-to-end GenAI architectures for enterprise applications, including chatbot systems, copilots, document intelligence, recommendations, and AI automation workflows.
Evaluate and select appropriate foundation models (OpenAI, Anthropic, Gemini, open-source LLMs, etc.) based on business and technical requirements.
Build and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases and embedding models.
Define scalable AI infrastructure on cloud platforms such as AWS, Azure, or Google Cloud.
Collaborate with data engineers, ML engineers, product managers, and business stakeholders to translate requirements into AI solutions.
Establish best practices for prompt engineering, fine-tuning, model evaluation, observability, and inference optimization.
Ensure AI solutions comply with security, governance, privacy, and responsible AI standards.
Design APIs, orchestration layers, and integration patterns for enterprise applications.
Monitor model performance, hallucination risks, latency, and operating costs.
Monitor model performance, hallucination risks, latency, and operating costs.
Stay current with advancements in LLMs, AI agents, multimodal AI, and AI tooling ecosystems.

Required Skills and Qualifications:

Hands-on experience with LLM frameworks such as LangChain, LlamaIndex, Semantic Kernel, or similar.
Strong understanding of transformer models, embeddings, vector stores, and RAG architectures.
Experience with Python and AI development libraries.
Familiarity with AI platforms such as Azure OpenAI, AWS Bedrock, Vertex AI, or OpenAI APIs.
Experience designing scalable microservices and distributed systems.
Knowledge of DevOps/MLOps practices including CI/CD, Docker, Kubernetes, monitoring, and model lifecycle management.
Understanding of AI governance, security, and compliance considerations.
Excellent communication and stakeholder management skills.

Preferred Qualifications:

Experience fine-tuning open-source LLMs.
Exposure to agentic AI systems and AI workflow orchestration tools.
Knowledge of graph databases, knowledge graphs, or semantic search.
Certifications in cloud platforms or AI technologies.
Experience leading enterprise AI transformation initiatives.

Typical Technologies:

Languages: Python, JavaScript, Java
Frameworks: LangChain, LlamaIndex, Hugging Face Transformers
LLM Models: Hugging Face Transformers
Vector Databases: Pinecone, Weaviate, FAISS, Chroma
Cloud Platforms: Azure, AWS, Google Cloud Platform
DevOps Tools: Docker, Kubernetes, Terraform, GitHub Actions
Monitoring & Evaluation: LangSmith, MLflow, PromptFlow, OpenTelemetry