Job Title

Company Description xponent.ai is Sydney, Australia based AI-native startup, and a Databricks Partner, building and delivering Data and AI solutions accelerating measurable outcomes for enterprises. By combining industry expertise with Data and AI, we deliver impactful, solutions. Our robust delivery playbooks, along with pre-built assets and accelerators, allow for seamless transformation at any stage of an organization’s data and AI journey.Role Description This is a full-time role based out of India for someone with 2-3 years of experience as ML Engineer in Databricks environment, someone who is excited about building real- world, production-grade AI systems — not just PoC/, we work with clients to design and deliver modern data platforms and AI solutions — from scalable feature pipelines to agentic AI applications. This role sits at the intersection of data engineering, ML engineering, and applied AI. You will work closely with consultants, data engineers, and clients to move from problem → solution → production.What You’ll Work On Build scalable data and feature pipelines using PySpark on Databricks Design and manage end-to-end ML workflows — from experimentation to production Develop and deploy deep learning models at scale Build and productionise GenAI applications (RAG systems, LLM APIs, prompt pipelines) Enable real-time and batch ML use cases across enterprise environments Collaborate directly with consulting teams to translate business problems into ML solutionsCore Requirements PySpark & Distributed Data Processing Hands-on experience building scalable pipelines using PySpark Strong understanding of distributed data processing and performance optimization Experience working with large datasets in production-like environment MLflow & Model Lifecycle Management Proficiency with ML flow for:Experiment tracking, Model versioning, Model registry, Deployment workflows Understanding of reproducibility and governance in ML systems. Deep Learning & Distributed Training Hands-on experience with frameworks such as PyTorch / TensorFlow Ability to scale training using GPU clusters (preferably Databricks Mosaic AI or similar) Understanding of training optimisation, tuning, and evaluation at scale. GenAI & LLM Deployment Practical experience with: o Prompt engineering o RAG architectures o Fine-tuning/ adapting LLMs Experience deploying LLMs via APIs or model serving platforms (Databricks Model Serving preferred) Understanding of latency, cost, and evaluation trade-offs in LLM systems Feature Engineering & Feature Store Ability to design and compute robust, reusable features at scale Experience with Databricks Feature Store or similar systems Understanding of online vs offline feature serving patternsWhat We’re Looking For 2–3 years of hands-on experience in ML Engineering roles Strong fundamentals in data structures, ML concepts, and system design Ability to work across data, ML, and application layers Comfortable working in fast-paced, ambiguous environments Strong communication — able to explain technical decisions clearlyNice to Have Experience with Databricks Lakehouse architecture Exposure to real-time / streaming ML pipelines Experience working in consulting or client-facing environments Understanding of AI governance, evaluation, and responsible AI practices