KEY RESPONSIBILITIES :

- Define and drive the overall AI/ML architecture strategy across the organization

- Design scalable, resilient, and secure AI pipelines and platforms

- Lead the evaluation and adoption of AI frameworks, LLM APIs, and vector databases

- Collaborate with Product, Engineering, and Business leaders to translate requirements into technical roadmaps

- Establish best practices for model lifecycle management, MLOps, and responsible AI

- Conduct architecture reviews, code reviews, and technical design sessions

- Mentor senior engineers and provide technical leadership across squads

- Drive proof-of-concepts (PoCs) for emerging AI technologies and recommend adoption

- Ensure compliance with data privacy regulations (GDPR, DPDPA) in AI systems

- Own non-functional requirements: scalability, latency, availability, and cost optimization

KEY SKILLS & REQUIREMENTS :

- Deep expertise in AI/ML architecture patterns: RAG, agentic AI, multi-model orchestration

- Proficiency with LLM ecosystems: OpenAI, Anthropic Claude, Google Gemini, open-source LLMs (Llama, Mistral)

- Strong knowledge of vector databases: Pinecone, Weaviate, pgvector, ChromaDB

- Cloud architecture expertise: AWS, GCP, or Azure (AI/ML services SageMaker, Vertex AI, Azure OpenAI)

- Experience with MLOps tools: MLflow, Kubeflow, Weights & Biases

- Proficiency in Python for AI/ML; working knowledge of Node.js or Java for backend integration

- Knowledge of data engineering: Spark, Kafka, dbt, Airflow

- Strong understanding of microservices, API design, and event-driven architecture

- Familiarity with AI governance, model evaluation frameworks, and prompt engineering

- Experience with containerization: Docker, Kubernetes, Helm

NICE TO HAVE :

- Experience with agentic frameworks: LangChain, LlamaIndex, AutoGen, CrewAI

- Published research or open-source contributions in AI/ML

- Exposure to multi-modal AI (vision, voice, video) systems

- Prior startup or product-led company experience