Description

Required Skills

• Strong understanding of Machine Learning, Deep Learning, Generative AI, LLMs/SLMs, RAG, Agentic AI, prompt/context engineering, embeddings, vector search, structured outputs, and AI evaluation techniques.
• Strong hands-on knowledge of Python and AI/ML frameworks such as PyTorch, TensorFlow, Hugging Face, LangChain, LangGraph, or CrewAI.
• Experience with enterprise AI and cloud platforms such as Azure AI Foundry/Azure Machine Learning, AWS SageMaker and related AWS AI services, or Google Vertex AI.
• Knowledge of SQL, NoSQL, vector databases, graph databases, knowledge graphs, and data/platform technologies such as Kafka, Spark, and Airflow.
• Experience with cloud-native engineering and MLOps/LLMOps, including Docker, Kubernetes, Terraform, CI/CD, GitOps, MLflow or equivalent lifecycle and observability platforms.
• Strong understanding of REST/GraphQL APIs, microservices, event-driven architectures, distributed systems, enterprise integration patterns, and architecture for security, privacy, resiliency, scalability, performance, observability, and cost optimization.
• Experience with Spec-Driven Engineering, including requirements decomposition, architecture and API specifications, executable acceptance criteria, specification-to-code/test traceability, version control, and AI-assisted engineering workflows.
• Strong client-facing solutioning, stakeholder management, technical leadership, architecture documentation, and communication skills, with experience designing AI solutions for regulated industries.
• Knowledge of knowledge graphs, ontologies and Graph-RAG architectures

Key Responsibilities

• Architect end-to-end enterprise AI solutions spanning data ingestion, model/LLM integration, retrieval, orchestration, application integration, deployment, evaluation, monitoring, and governance.
• Design Generative AI, RAG, and Agentic AI solutions.
• Define Agentic AI architectures incorporating agent orchestration, tool/API integration, context and memory management, human-in-the-loop controls, guardrails, evaluation, and observability.
• Evaluate and select appropriate LLMs, SLMs, embedding models, AI frameworks, retrieval approaches, and deployment patterns based on accuracy, latency, security, scalability, and cost.
• Architect AI solutions using services across AWS, Microsoft Azure, and Google Cloud, with deep expertise in at least one hyperscaler.
• Design cloud-native and distributed architectures using APIs, microservices, event-driven patterns, containers, and Kubernetes.
• Define enterprise AI security and governance controls covering identity and access, data privacy, responsible AI, model security, auditability, and regulatory requirements.
• Lead solution discovery, architecture definition, technical assessments, POCs, reference architectures, reusable accelerators, and productionization.
• Drive Spec-Driven Engineering across the AI solution lifecycle by translating business intent, architecture decisions, functional and non-functional requirements, interfaces, guardrails, and acceptance criteria into version-controlled, machine-consumable specifications that guide design, code generation, testing, validation, and deployment.
• Partner with business stakeholders, enterprise architects, product teams, data scientists, AI engineers, DevOps/platform teams, and security teams.
• Provide technical leadership, conduct architecture/design reviews, mentor engineering teams, and communicate solution trade-offs to technical and executive stakeholders

Key Skills

Education

Bachelor's degree