AI Architect

Key Responsibilities

• Design and develop scalable, secure, and enterprise-grade AI architectures.
• Define AI/ML strategies, technical roadmaps, and architecture standards.
• Lead the design and implementation of Generative AI and LLM-based applications.
• Develop and implement Retrieval-Augmented Generation (RAG) architectures.
• Design and integrate LLM solutions using OpenAI, Azure OpenAI, AWS Bedrock, or Google Vertex AI.
• Build AI-powered applications using Python, machine learning frameworks, and modern AI technologies.
• Design and implement intelligent chatbots, virtual assistants, and conversational AI solutions.
• Work with vector databases and embedding models for semantic search and knowledge retrieval.
• Design scalable machine learning pipelines for model training, evaluation, deployment, and monitoring.
• Integrate AI/ML solutions with enterprise systems, APIs, databases, and cloud platforms.
• Establish best practices for AI governance, security, responsible AI, and data privacy.
• Optimize AI models for performance, scalability, latency, and cost efficiency.
• Evaluate emerging AI technologies, frameworks, and tools to identify opportunities for innovation.
• Collaborate with data scientists, ML engineers, software developers, and business stakeholders.
• Provide technical leadership, architecture guidance, and mentorship to development teams.
• Ensure AI solutions meet enterprise security, compliance, and operational requirements.

Required Technical Skills

• 8+ years of experience in software engineering, AI/ML engineering, data science, or related technical roles.
• Strong experience designing and implementing enterprise AI/ML architectures.
• Strong programming experience in Python.
• Hands-on experience with Machine Learning and Deep Learning concepts.
• Strong understanding of Generative AI and Large Language Models (LLMs).
• Experience developing RAG-based applications and LLM-powered solutions.
• Experience with OpenAI, Azure OpenAI, AWS Bedrock, or Google Vertex AI.
• Knowledge of prompt engineering, embeddings, tokenization, and model fine-tuning.
• Experience with vector databases such as Pinecone, FAISS, Weaviate, or ChromaDB.
• Experience with AI/ML frameworks such as TensorFlow, PyTorch, Scikit-learn, or Hugging Face.
• Strong understanding of REST APIs, microservices, and distributed systems.
• Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform.
• Knowledge of model deployment, monitoring, and MLOps practices.
• Understanding of data engineering, data pipelines, and enterprise data integration.
• Strong architecture, problem-solving, and technical leadership skills.

Preferred Skills

• Experience with LangChain, LangGraph, LlamaIndex, or Semantic Kernel.
• Knowledge of Agentic AI and multi-agent architectures.
• Experience with autonomous AI agents and tool calling.
• Experience with Kubernetes, Docker, and containerized deployments.
• Knowledge of MLflow, Kubeflow, or similar MLOps platforms.
• Experience with NVIDIA AI technologies and GPU-based computing.
• Knowledge of Responsible AI, explainability, and AI governance frameworks.
• Experience with real-time AI applications and event-driven architectures.
• Familiarity with Databricks, Snowflake, and enterprise data platforms.
• Experience with CI/CD pipelines, Terraform, and Infrastructure as Code.
• Knowledge of fine-tuning techniques such as LoRA and PEFT.
• Experience implementing AI observability, evaluation, and guardrails.