Gen AI / ML Architect

Responsibilities:

• Define and own the overall architecture for generative AI platforms, including LLMs, data layers, knowledge systems, and agent orchestration frameworks.
• Design scalable, cloud-native architectures that support high-performance AI workloads.
• Establish architectural patterns for retrieval-augmented generation (RAG), reasoning, evaluation, and observability.
• Ensure non-functional requirements such as reliability, security, compliance, latency, and cost efficiency are met.
• Provide technical leadership and guidance to engineering teams across AI, data, and platform domains.
• Design and maintain reference architectures for AI-driven data insight products.
• Collaborate with product managers to align architecture with business objectives.
• Partner with security and compliance teams to embed governance-by-design.
• Review designs and implementations to ensure architectural consistency and quality.
• Identify technical risks early and propose scalable, sustainable solutions.

Requirements:

• Master's degree or PhD in computer science, software engineering, or a related field.
• Minimum of 8-12 years of experience in software engineering, data platforms, and AI systems.
• Prior experience designing and delivering enterprise-scale AI or data products.
• Hands-on experience with cloud platforms and modern AI technologies is strongly preferred.
• Strong experience designing distributed, cloud-native systems.
• Deep understanding of generative AI, large language models, embeddings, and agent-based architectures.
• Experience with data platforms, APIs, and AI integration patterns.
• Knowledge of AI evaluation, monitoring, and observability frameworks.
• Familiarity with security, privacy, and governance considerations for AI systems.

Technical Skills Required:

• Distributed Systems Design.
• Search architecture (OpenSearch / Elasticsearch).
• API Design (REST / FastAPI).
• Cloud-native architecture (AWS / GCP / Azure).
• Kubernetes fundamentals.
• Data modeling at scale.
• Multi-tenant SaaS design preferred.
• RAG-architecture understanding preferred.
• Vector search familiarity preferred.
• Event-driven systems (Kafka / PubSub) preferred.
• Security-aware design preferred.