AI Architect

In-Person Interview Required

We are seeking an experienced and visionary AI Architect to design, architect, and deliver enterprise-scale Artificial Intelligence solutions. In this role, you will be responsible for defining the end-to-end AI/ML strategy, designing robust infrastructure for Generative AI (GenAI) and Agentic workflows, ensuring scalable model deployment (LLMOps/MLOps), and embedding governance and ethical AI frameworks into core technology ecosystems.

Responsibilities:

• Design and implement scalable, enterprise-grade AI/ML architectures, including RAG (Retrieval-Augmented Generation), fine-tuned LLMs, multi-agent orchestrations, and predictive analytics platforms.
• Architect solutions leveraging advanced LLMs, Vector Databases, Prompt Engineering, and Agentic AI frameworks (e.g., LangChain, LlamaIndex, AutoGen, CrewAI).
• Establish end-to-end pipelines for model training, evaluation, continuous deployment, monitoring, guardrails, latency optimization, and cost governance.
• Architect cloud-native AI infrastructures using AWS (Bedrock, SageMaker), Azure (Azure OpenAI, Databricks), or Google Cloud Platform (Vertex AI), ensuring high availability, security, and low latency.
• Collaborate with Data Engineering teams to design secure feature stores, vector storage, context retrieval systems, and RESTful APIs/Microservices for AI integration.
• Implement Responsible AI practices, data privacy (PII masking), model safety/guardrails (NeMo Guardrails, Llama Guard), compliance standards, and risk mitigation strategies.
• Partner with CTO office, Enterprise Architects, Product Owners, and Security teams to align technical AI roadmaps with business objectives.

Requirement:

• Deep expertise in Generative AI, Large Language Models (LLMs), Embeddings, Fine-Tuning (LoRA, QLoRA), and RAG architectures.

• Hands-on experience building multi-agent AI systems, tool-calling pipelines, and autonomous workflows.

• Strong knowledge of modern AI frameworks: PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex, Semantic Kernel.

• Architecture-level experience with cloud AI suites: AWS (SageMaker, Bedrock), Azure (Azure AI Studio, OpenAI Service, Databricks), or Google Cloud Platform (Vertex AI).

• Deep understanding of vector databases (Pinecone, Milvus, Qdrant, Chroma, pgvector).

• Solid experience with MLOps tools (MLflow, Kubeflow, Weights & Biases) and CI/CD pipelines for AI model lifecycle management.

• Strong proficiency in Python, C++, or Java/Go, along with RESTful APIs, gRPC, microservices architecture, and containerization (Docker, Kubernetes).

• Experience with distributed computing frameworks (Spark, Ray, Dask).

• Understanding of AI safety, prompt injection defenses, halluciantion detection, and data security standards (SOC 2, GDPR, HIPAA compliance in AI).