Specialist Solutions Architect | AI and ML

Databricks is the lakehouse data and AI platform, and this specialist solutions architect role is the technically deepest customer facing position in today's list. You architect production grade machine learning and AI workloads for customers on the platform, covering agents, end to end ML pipelines, training and inference optimisation, integration with cloud native services and MLOps. The posting also expects you to act as a practitioner for enterprise generative AI, naming retrieval augmented generation architectures, agentic systems with tool calling and multi agent orchestration, guardrails, and natural language querying of structured data. Stated bar is five or more years of hands on industry machine learning experience. The posting says clearly it is a hybrid role based in Mumbai, and mentorship and thought leadership are part of it.

Published requirement is 5+ years of hands on industry machine learning experience in at least one of two named tracks. The ML engineer track: building and maintaining production grade cloud infrastructure on AWS, Azure or GCP that supports deployment of ML applications, including drift monitoring. The AI engineer track: experience with current techniques in large language models and agentic systems, including vector databases, fine tuning LLMs, AI guardrail systems, and deploying LLMs with tooling such as HuggingFace, LangChain and OpenAI. Also required: a graduate degree in a quantitative discipline such as computer science, engineering, statistics or operations research, or equivalent practical experience; and experience communicating or teaching technical concepts to both technical and non technical audiences. A preferred rather than required item is 2+ years of customer facing experience in a pre sales or post sales role, which is worth noting because it means deep customer experience is not a gate, so a strong ML engineer can move into this role without a solutions background. The posting states directly that this is a hybrid role based in Mumbai. Responsibilities: architect production level ML and AI workloads for customers on the platform, including agents, end to end ML pipelines, training and inference optimisation, integration with cloud native services and MLOps; and serve as a trusted practitioner for enterprise generative AI solutions covering RAG architectures, agentic systems with tool calling agents and multi agent orchestration, guardrails, and natural language querying of structured data. Mentorship and establishing yourself as an AI thought leader are also named. Fit guidance: this is the most technical customer facing role in today's edition and the closest thing to an engineering job among them. If you are an ML engineer who is tired of one narrow model and wants exposure to many enterprise architectures, it fits well. Mumbai is a rare location for a role of this seniority.