AI Architect

Mandatory Skills & Expected Experience1. GenAI & LLM Architecture

• Strong hands-on experience designing enterprise GenAI and LLM architectures.
• Strong expertise in RAG, embeddings, vector databases, prompt engineering and AI agents.
• Experience with LLM fine-tuning, guardrails and hallucination mitigation.
• Experience with LLMs such as OpenAI, LLaMA, Gemini or equivalent.
• Ability to design scalable GenAI solutions from POC to production.

2. AI/ML & Data Engineering

• Strong understanding of Machine Learning algorithms, model evaluation and experimentation.
• Hands-on experience with feature engineering and ML pipelines.
• Strong understanding of structured and unstructured data processing.
• Experience with Python and PySpark.
• Knowledge of TensorFlow, PyTorch or Scikit-learn.
• Understanding of model explainability, bias/fairness and responsible AI.

3. AI Platform & Enterprise Architecture

• Proven experience designing end-to-end enterprise AI architectures.
• Strong understanding of integration patterns, APIs, microservices and distributed systems.
• Experience architecting batch and real-time AI inference solutions.
• Strong understanding of scalability, security, governance and resilience.
• Experience with multi-cloud or cloud-native architecture is expected.

4. Databricks / SnowflakeStrong hands-on experience with at least one of:

• Databricks / Databricks Mosaic AI
• Snowflake / Snowflake Cortex

Experience should include relevant areas such as:

• Data engineering and transformation
• ML/AI pipelines
• Lakehouse architecture
• AI/ML platform integration
• Data governance and security
• Performance and cost optimization

5. RAG & Agentic AI

• Strong experience designing enterprise RAG architectures.
• Expertise in chunking, embeddings, retrieval, reranking and grounding.
• Hands-on experience with Vector Databases such as FAISS, Pinecone, Weaviate or equivalent.
• Experience designing AI agents, tool calling, function calling and workflow orchestration.
• Exposure to multi-agent architectures is preferred.

6. Cloud & AI Platform ArchitectureExperience architecting AI solutions on one or more:

• AWS
• Microsoft Azure
• Google Cloud Platform
• Azure OpenAI / Azure AI Foundry
• AWS Bedrock
• Google Vertex AI

Strong understanding of cloud-native architecture, security and scalability is expected.7. MLOps / LLMOps

• Strong understanding of MLOps and LLMOps lifecycle management.
• Experience with MLflow, Kubeflow, Azure ML or equivalent.
• Experience with CI/CD, model/version management and deployment.
• Experience with monitoring, evaluation, rollback and lifecycle management.
• Hands-on exposure to Docker and Kubernetes.

8. Observability, Performance & Cost Optimization

• Experience implementing AI/LLM observability and evaluation frameworks.
• Understanding of tracing, model quality monitoring and feedback loops.
• Experience optimizing LLM latency, throughput and token consumption.
• Knowledge of model routing, caching and cost governance.
• Ability to balance quality, performance, scalability and cost.

9. Security, Governance & Responsible AI

• Experience designing secure enterprise AI solutions.
• Strong understanding of AI governance, data privacy, model risk and compliance.
• Experience implementing guardrails, access controls and responsible AI practices.
• Understanding of explainability, auditability and model monitoring.

10. Architecture Leadership & Communication

• Ability to conduct architecture reviews and technical assessments.
• Experience mentoring AI, Data Science and Engineering teams.
• Strong stakeholder management and executive communication skills.
• Ability to translate business requirements into scalable AI architecture.
• Experience driving technology decisions and influencing enterprise AI strategy.

Key Responsibilities

• Define future-state AI/GenAI architecture and technology roadmap.
• Review existing product architecture and identify opportunities for AI-driven transformation.
• Design enterprise-grade LLM, RAG and Agentic AI solutions.
• Lead technical POCs to validate architecture and emerging AI technologies.
• Define architecture standards, reusable frameworks and engineering best practices.
• Work with Data Engineering, Data Science, Product and Engineering teams to drive implementation.
• Ensure AI solutions meet requirements around security, governance, scalability, performance and cost.
• Provide technical leadership and guide teams through architecture and implementation decisions.

Preferred Candidate Profile

• 12+ years of overall technology experience with significant experience in AI/ML, Data or Architecture.
• Strong recent experience in GenAI/LLM architecture and enterprise AI solutions.
• Strong combination of GenAI + RAG + Agentic AI + Python + Cloud + Databricks/Snowflake.
• Proven experience taking AI solutions from POC architecture production.
• Strong enterprise architecture and stakeholder management experience.
• Candidates with experience in Databricks Mosaic AI, Snowflake Cortex, Azure AI Foundry, AWS Bedrock or Vertex AI will be preferred.

Core Mandatory StackGenAI / LLMs | RAG | AI Agents | Vector DB | Python | AI/ML Architecture | Databricks / Snowflake | Cloud AI | MLOps / LLMOps | Enterprise ArchitecturePreferred: Azure OpenAI / AI Foundry | AWS Bedrock | Vertex AI | Databricks Mosaic AI | Snowflake Cortex | MLflow | Docker/Kubernetes | LangSmith/LangFuseRole Fitment ImportantThis is an Architecture + Hands-on AI role. Candidates should have demonstrable experience in designing and implementing AI/GenAI solutions, rather than profiles focused only on people management, strategy or high-level architecture.Hands-on GenAI + Enterprise Architecture + RAG/Agents + Databricks/Snowflake + Cloud experience is mandatory.