Ml Platform Engineer in Bengaluru

We're hiring an AI Support Engineer to work directly with the founder and build the systems that power customer support at Bolna. This isn't a traditional support role — you'll use AI to make support scale, and you'll partner closely with the business team on the customer conversations that matter most. What you'll do - Work directly with the founder to design and continuously improve how customer support runs at Bolna - Pull and collate data from Intercom to spot patterns, recurring issues, and gaps in how customers are being helped - Build AI-powered workflows that triage, answer, and resolve customer support queries with less manual effort - Design the systems and processes behind a streamlined, scalable support flow — from triage to escalation to resolution - Step in directly on critical customer support situations alongside the business team when it matters - Turn recurring support themes into feedback for product and engineering What we're looking for - 1–3 years of experience in a support, ops, or technical customer-facing role — ideally somewhere that rewarded building your own tools and process, not just following a playbook - Hands-on comfort with AI tools/workflows (prompting, automations, agent builders) — you don't need to be an ML engineer, but you should be someone who reaches for AI to solve a workflow problem - Experience with Intercom or a similar support/helpdesk tool - Sharp, structured communicator — equally comfortable writing to customers and to the founder - Comfortable with ambiguity — this role is being built as you build it Nice to have - Experience setting up support automations, chatbots, or AI agents in a real product company - Familiarity with SQL or basic scripting to pull/analyze support data - Startup experience, especially in a 0-to-1 function

About the Role Adobe is seeking a Machine Learning Engineer to join the Adobe Genuine Engineering team. This group protects Adobe's ecosystem from fraud, abuse, and misuse using intelligent systems worldwide. In this position, you will build and develop machine learning models from scratch, including custom transformer-based frameworks, to identify fraudulent actions, stop account sharing, and protect the experience of hundreds of millions of users. You will manage the entire model lifecycle: raw behavioral data and feature engineering, architecture development, large-scale GPU training, deployment, and monitoring. The team is actively building in-house behavioral foundation models that learn identity-preserving representations from long sequences of user activity. This is a role for an engineer who wants to own deep learning systems end-to-end — not consume pre-built ones. Key Responsibilities Build and train deep learning models from scratch, including custom transformer and attention-based architectures for long behavioral event sequences. Own the full training stack: event tokenization, temporal and positional embeddings, self-supervised pretraining (e.g., masked modeling, contrastive learning), and downstream fine-tuning. Train large models efficiently on GPU infrastructure using mixed-precision training, gradient accumulation/checkpointing, efficient attention, and distributed strategies (DDP, FSDP, or equivalent). Build and optimize feature pipelines on Databricks and Spark, transforming raw behavioral events into high-quality model inputs. Translate prototypes into production ML systems — scalable, reliable, and observable — and drive inference performance through architectural and serving-side optimization. Contribute to MLOps practices: experiment tracking, model versioning, CI/CD, automated retraining, and