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 LangGraph, LangChain, 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.