AI Lead / AI Architect

AI Lead / AI Architect AI Lead / AI Architect with a minimum of 7 years of experience in AI/ML, data science, software engineering, or related fields , including strong hands-on experience designing and delivering enterprise AI/ML solutions . Will provide technical leadership for AI initiatives, define AI architecture and solution patterns, and guide AI/ML solutions from business requirements through production deployment.

Key Responsibilities

Define the AI/ML architecture and technical strategy for enterprise AI initiatives. Translate business objectives and use cases into scalable, secure, and production-ready AI solutions. Lead the design of end-to-end AI architectures , including models, data, APIs, applications, infrastructure, and integration components.

Evaluate AI/ML technologies, frameworks, platforms, and models and recommend the most appropriate solutions. Establish reusable AI architecture patterns, standards, and best practices . Define technical approaches for traditional ML, Generative AI, LLMs, RAG, and AI Agents. Work closely with Business Analysts and Product teams to assess AI use cases, technical feasibility, value, data requirements, and dependencies.

Guide Data Scientists and AI/ML Engineers in model development, experimentation, evaluation, and productionization. Work with Data Engineers to define data architecture and pipelines required for AI/ML solutions. Work with Backend and Frontend teams to integrate AI capabilities into enterprise applications. Work with DevOps/MLOps teams to establish production deployment architectures across on-premises Kubernetes and cloud environments .

Define requirements for model serving, inference, scalability, monitoring, and lifecycle management. Establish appropriate approaches for model versioning, deployment, monitoring, retraining, and rollback . Ensure AI solutions meet requirements for security, performance, scalability, availability, and maintainability.

Identify technical risks, architectural constraints, dependencies, and technology gaps. Lead technical design reviews and provide guidance on complex AI/ML architecture decisions. Ensure AI solutions are aligned with enterprise architecture and security standards. Provide technical leadership and mentorship to AI/ML and engineering teams.

Stay current with developments in Generative AI, LLMs, AI Agents, MLOps, and AI engineering and assess their relevance to the organization. Generative AI G LLM Design architectures for LLM-powered applications and enterprise Generative AI solutions. Define appropriate approaches for: RAG Embeddings Vector databases Prompt engineering LLM orchestration AI Agents Tool/function calling Model routing and selection Evaluate commercial and open-source models based on performance, cost, latency, security, and business requirements .

Design solutions that support multiple LLM providers or models where appropriate. Establish approaches for LLM evaluation, monitoring, and quality measurement. Address AI-specific concerns such as hallucination, data leakage, prompt injection, access control, and model reliability . Guide teams in selecting appropriate frameworks such as Lang.

Graph, Lang. Chain, or similar AI application frameworks . AI/ML Architecture Design end-to-end architectures covering: Data → ML/AI Model → Model Serving → API → Application → Monitoring Define appropriate model development and production environments. Establish patterns for real-time and batch inference. Define integration patterns for AI/ML models with enterprise applications and APIs.

Ensure AI solutions can operate across on-premises, cloud, and hybrid environments . Design for scalability and high availability of AI/ML workloads. Define appropriate compute requirements, including CPU/GPU resources where applicable. Establish architecture patterns for AI/ML observability and monitoring. MLOps G Productionization Define the MLOps architecture and lifecycle for AI/ML solutions.

Work with DevOps/MLOps teams to implement automated ML/AI deployment pipelines. Establish standards for: Model versioning Model registry Experiment tracking Model deployment Model monitoring Model rollback Model retraining Guide the deployment of models to Kubernetes-based environments . Ensure AI/ML solutions can be deployed consistently across development, testing, and production.

Define requirements for model performance monitoring and detection of data/model drift . Support the transition of prototypes and proof-of-concepts into production-grade solutions. AI Governance G Responsible AI Define technical controls for AI security, privacy, governance, and responsible AI . Establish appropriate human oversight for AI-powered decisions.