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.