Senior Platform Engineer – MLops / Databricks

About the job I’m helping EROS Technologies Inc. find a top candidate to join their team full-time for the role of Senior Platform Engineer – MLops / Databricks. You will scale AI and ML production platforms by engineering robust cloud-native infrastructure. Compensation: Provide your expected compensation while applying Location: Remote (for residents of Argentina, Brazil, Chile, Colombia, Costa Rica, Ecuador, Mexico, Panama, Peru, and Uruguay) Mission of EROS Technologies Inc.: "Empower organizations with innovative workforce and IT solutions that strengthen operations and drive mission-critical success." What makes you a strong candidate: You have +7 years experience in Software engineering. You are an expert in Python, Node.js, Databricks, Back-end development, and API development. You are proficient in Artificial Intelligence and Machine Learning (AI/ML), Amazon Web Services (AWS), AWS Cloud Development Kit (AWS CDK), Continuous Integration and Continuous Deployment (CI/CD), Cloud-Native Architecture, AWS CloudFormation, Containerization, Machine Learning Operations (MLOps), MLflow, Retrieval-Augmented Generation (RAG) English - Fully fluent Responsibilities and more: Role: Senior Platform Engineer – MLOps / Databricks. Location: Anywhere in LATAM—Remote. Key Responsibilities: Design, implement, and maintain cloud-native platforms supporting AI and data workloads, with a focus on Databricks and AWS. Build and manage scalable data pipelines to ingest, transform, and serve data for ML and analytics. Develop Infrastructure-as-Code (IaC) solutions using CloudFormation and AWS CDK to ensure repeatable and secure deployments. Collaborate with AI engineers, data engineers, and platform teams to improve performance, reliability, and cost efficiency of AI models in production. Drive observability best practices, including monitoring, alerting, and logging across AI platforms. Contribute to the design and evolution of AI platforms that support new ML frameworks, workflows, and data types. Stay current with emerging tools and technologies and recommend improvements to platform architecture and operations. Integrate AI models and Large Language Models (LLMs) into production systems, including Retrieval-Augmented Generation (RAG) architectures. Minimum Qualifications: 7+ years of professional experience in software engineering and infrastructure engineering. Extensive experience building and maintaining AI/ML infrastructure in production environments, including model deployment and lifecycle management. Strong knowledge of AWS and Infrastructure-as-Code frameworks, ideally AWS CDK. Expert-level coding skills in Node.js and Python, with experience developing APIs and backend services. Proven experience with MLflow, including model registration, versioning, asset bundles, and model-serving workflows. Hands-on experience with Databricks and modern data platform technologies. Experience with CI/CD pipelines, containerization, and cloud-native architectures. Strong understanding of MLOps best practices and AI platform engineering. Preferred Skills: Databricks. MLflow. Python. Node.js. AWS CDK. CloudFormation. MLOps. LLMs & RAG. CI/CD. Docker. Kubernetes. Your potential leader: Shivani B