AI Lead / AI Architect

An experienced AI Lead / AI Architect with at least seven years in AI/ML, data science, software engineering, or related fields is sought to provide technical leadership in designing and delivering enterprise AI/ML solutions. This role involves defining AI architecture and solution patterns, guiding AI/ML initiatives from business requirements through to production deployment, and ensuring alignment with enterprise standards. The ideal candidate will have strong hands-on experience in developing scalable, secure, and production-ready AI systems, including expertise in Generative AI, large language models (LLMs), and MLOps.

Key Responsibilities

- Define the AI/ML architecture and technical strategy for enterprise AI initiatives, translating business objectives into scalable and secure AI solutions.
- Lead the design of end-to-end AI architectures encompassing models, data, APIs, applications, infrastructure, and integration components.
- Evaluate AI/ML technologies, frameworks, platforms, and models, recommending the most suitable solutions.
- Establish reusable AI architecture patterns, standards, and best practices.
- Define technical approaches for traditional ML, Generative AI, LLMs, retrieval-augmented generation (RAG), and AI Agents.
- Collaborate closely with Business Analysts and Product teams to assess AI use cases, 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 necessary for AI/ML solutions.
- Partner with Backend and Frontend teams to integrate AI capabilities into enterprise applications.
- Collaborate 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, including versioning, deployment, monitoring, retraining, and rollback.
- Ensure AI solutions meet security, performance, scalability, availability, and maintainability standards.
- Identify technical risks, architectural constraints, dependencies, and technology gaps.
- Lead technical design reviews and provide guidance on complex AI/ML architectural decisions.
- Ensure AI solutions align 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, assessing their relevance to the organization.

Generative AI and LLM-Specific Responsibilities

- Design architectures for LLM-powered applications and enterprise Generative AI solutions.
- Define approaches for RAG, embeddings, vector databases, prompt engineering, LLM orchestration, AI Agents, tool/function calling, and model routing/selection.
- Evaluate commercial and open-source models based on performance, cost, latency, security, and business requirements.
- Design solutions supporting multiple LLM providers or models where appropriate.
- Establish methods 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 frameworks like LangGraph, LangChain, or similar AI application frameworks.

AI/ML Architecture

- Design end-to-end architectures covering data ingestion, ML/AI model development, model serving, API integration, application layers, and monitoring.
- Define suitable 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 operate effectively across on-premises, cloud, and hybrid environments.
- Design for scalability and high availability of AI/ML workloads.
- Specify compute requirements, including CPU/GPU resources where applicable.
- Establish architecture patterns for AI/ML observability and monitoring.

MLOps and Productionization

- Define MLOps architecture and lifecycle for AI/ML solutions.
- Collaborate with DevOps/MLOps teams to implement automated ML/AI deployment pipelines.
- Establish standards for model versioning, registry, experiment tracking, deployment, monitoring, rollback, and retraining.
- Guide deployment of models to Kubernetes-based environments.
- Ensure consistent deployment across development, testing, and production environments.
- Define requirements for model performance monitoring and detection of data/model drift.
- Support transitioning prototypes and proof-of-concepts into production-grade solutions.

AI Governance and Responsible AI

- Define technical controls for AI security, privacy, governance, and responsible AI.
- Establish appropriate human oversight for AI-powered decisions.

Required Qualifications

- Minimum of 7 years of experience in AI/ML, data science, software engineering, or related fields.
- Proven hands-on experience designing and delivering enterprise AI/ML solutions.
- Strong expertise in AI architecture, including Generative AI, LLMs, RAG, and AI Agents.
- Experience working with cross-functional teams including Data Scientists, Data Engineers, Product teams, and DevOps/MLOps.
- Deep understanding of AI/ML production deployment on Kubernetes and cloud platforms.
- Solid knowledge of AI/ML lifecycle management, model serving, monitoring, and retraining.
- Familiarity with AI security, privacy, governance, and responsible AI principles.

Preferred Qualifications and Benefits

- Experience with AI application frameworks such as LangGraph or LangChain.
- Knowledge of vector databases, prompt engineering, and LLM orchestration.
- Ability to evaluate and integrate multiple LLM providers.
- Strong leadership skills with experience mentoring AI/ML teams.
- Commitment to staying updated with the latest AI/ML trends and technologies.

This role offers the opportunity to lead cutting-edge AI initiatives within a dynamic enterprise environment, driving innovation and delivering impactful AI solutions at scale.