2 Aiml Engineers Jobs

Chargebee is seeking a visionary and hands-on Director of Data Analytics, Science & AI Enablement to lead the creation and growth of a data function that powers enterprise-wide AI initiatives. You will play a crucial role in designing, building, and leading a cross-functional team responsible for enterprise data analytics, data science, data governance, and structured data enablement to support advanced AI/ML use cases. As the Director of Data Analytics, Science & AI Enablement, you will lead the development and deployment of machine learning, generative AI, recommendation systems, and predictive models to enhance product intelligence and automation. You will be responsible for building and scaling AI capabilities across the platform, including personalization, NLP, anomaly detection, and customer segmentation, while ensuring that the models are interpretable, ethical, and aligned with business and customer trust standards. Driving insights into user behavior, product performance, churn prediction, and lifecycle value using customer and usage data will be a critical aspect of your role. You will develop dashboards, KPIs, and self-service analytics tools for marketing, product, sales, and support teams, as well as own the customer analytics roadmap to enhance onboarding, conversion, retention, and upsell opportunities. You will build and lead a high-performance team of data scientists, AI/ML engineers, analysts, and data product managers. Collaborating with various departments such as Product, Engineering, Marketing, Sales, Legal, Risk & Compliance, and Customer Success, you will align data strategy with business objectives and legal requirements. Your ability to communicate findings to senior leadership and influence roadmap decisions using data-backed recommendations will be crucial. Collaborating with Data Engineering, you will ensure scalable data architecture and high-quality data pipelines. Overseeing data quality, governance, and compliance across all analytical and operational systems, you will implement scalable data architecture and governance frameworks, ensuring compliance with data privacy regulations and internal standards. Additionally, you will establish and lead a high-performing global team of data analysts, data scientists, and data engineers. Driving data availability, quality, and governance across the organization to support AI and advanced analytics initiatives will be a key part of your role. You will partner with engineering, product, and business stakeholders to identify opportunities for AI/ML solutions and ensure they are supported by reliable, well-structured data. Serving as a thought leader for data science, analytics, and AI enablement best practices, you will lead the development of dashboards, metrics, and decision-support tools that empower business leaders. To be successful in this role, you should have a Bachelor's or Master's degree in Computer Science, Statistics, Data Science, Engineering, or related discipline. You should possess proven experience working in a SaaS or tech environment with subscription-based metrics, at least 10+ years of experience in data analytics, data science, or related fields, with 3-5 years in a leadership capacity. Strong knowledge of AI/ML concepts, data platforms, and BI tools, as well as deep understanding of data governance, data quality, and metadata management, are essential. Excellent communication and stakeholder management skills, along with prior experience with product instrumentation and event tracking platforms, will be beneficial for this role.,

The role requires you to design, develop, and maintain complex, high-performance, and scalable MLOps systems that interact with AI models and systems. You will collaborate with cross-functional teams, including data scientists, AI researchers, and AI/ML engineers, to comprehend requirements, define project scope, and ensure alignment with business goals. Your expertise will be crucial in selecting, evaluating, and implementing software technologies, tools, and frameworks within a cloud-native (Azure + AML) environment. Troubleshooting and resolving intricate software issues to ensure optimal performance and reliability when interfacing with AI/ML systems is an essential part of your responsibilities. Additionally, you will contribute to software development project planning and estimation, ensuring efficient resource allocation and timely solution delivery. Your role involves contributing to the development of continuous integration and continuous deployment (CI/CD) pipelines, high-performance data pipelines, storage systems, and data processing solutions. You will drive the integration of GenAI models, such as LLMs and foundation models, into production workflows, including overseeing orchestration and evaluation pipelines. Moreover, you will provide support for edge deployment use cases through model optimization, conversion (e.g., to ONNX, TFLite), and containerization for edge runtimes. Your contribution to creating and maintaining technical documentation, including design specifications, API documentation, data models, data flow diagrams, and user manuals, will be vital for effective communication within the team. **Required Qualifications:** - Bachelor's degree in software engineering/computer science or related discipline - Minimum of 6 years of experience in machine learning operations or software/platform development - Strong familiarity with Azure ML, Azure DevOps, Blob Storage, and containerized model deployments on Azure - Proficiency in programming languages commonly used in AI/ML, such as Python, R, or C++ - Experience with Azure cloud platform, machine learning services, and industry best practices **Preferred Qualifications:** - Experience with machine learning frameworks like TensorFlow, PyTorch, or Keras - Familiarity with version control systems like Git and CI/CD tools such as Jenkins, GitLab CI/CD, or Azure DevOps - Knowledge of containerization technologies such as Docker and Kubernetes, along with infrastructure-as-code tools like Terraform or Azure Resource Manager (ARM) templates - Exposure to Generative AI workflows, including prompt engineering, LLM fine-tuning, or retrieval-augmented generation (RAG) - Understanding of GenAI frameworks like LangChain, LlamaIndex, Hugging Face Transformers, and OpenAI API integration - Experience in deploying optimized models on edge devices using ONNX Runtime, TensorRT, OpenVINO, or TFLite - Hands-on experience with monitoring LLM outputs, feedback loops, or LLMOps best practices - Familiarity with edge inference hardware such as NVIDIA Jetson, Intel Movidius, or ARM Cortex-A/NPU devices This is a permanent position requiring in-person work.,