About the role
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
- 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
- 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