Role Description - This is a remote position. - Support the development and maintenance of company-wide frameworks and libraries that enable faster, better, and more informed decision-making within the business, creating significant business value from data and analytics. - Ensure data availability and accessibility for prioritized Advanced Analytics scope, and maintain stable, scalable, and modular data science pipelines from data exploration to deployment. - Acquire, ingest, and process data from multiple sources and systems into our cloud platform, especially AWS, ensuring data integrity and security. - Collaborate with data scientists to map data fields to hypotheses, and curate, wrangle, and prepare data for advanced analytical models. - Implement and manage robust security measures to ensure compliant handling and management of data, including access strategies aligned with Information Security, Cyber Security, and Data Privacy principles. - Develop and deploy smart automation tools based on cloud technologies, aligned with business priorities and needs. - Oversee the timely delivery of Advanced Analytics solutions in collaboration with business stakeholders, ensuring alignment with business goals. - Collaborate closely with the Data Science team and AI Engineers to understand their platform needs and lead the development of solutions that support their work. - Troubleshoot and resolve issues related to the AWS platform, ensuring minimal downtime and optimal performance. - Define and document best practices and strategies regarding application deployment and infrastructure maintenance. - Drive continuous improvement of the AWS Cloud platform by contributing and implementing new ideas and processes. Qualifications - At least 4+ years of relevant experience, with a strong track record in deploying solutions and applications on an AWS cloud environment. - Proven ability to work across structured, semi-structured, and unstructured data, extracting information and identifying linkages across disparate data sets. - Proficiency in multiple programming languages such as JavaScript, Python, Scala, or Java. - Extensive knowledge and experience with various database technologies, including distributed processing frameworks, relational databases, MPP databases, and NoSQL data stores. - Deep understanding of Information Security principles to ensure compliant handling and management of data. - Significant experience with cloud platforms, preferably AWS and its ecosystem. - Advanced knowledge of development in CI/CD environments. - Strong background in data warehousing and ETL tools. - Proficiency in DevOps practices and tools such as Jenkins, Terraform, and related technologies. - Proficiency in serverless architecture and services such as AWS Lambda. - Understanding of security best practices and implementation in cloud environments. - Ability to understand business objectives and create cloud-based solutions to meet those objectives. - Result-driven, analytical, and creative thinker. - Proven ability to work with cross-functional teams and bridge the gap between business and data science. - Fluency in English is a must; additional languages are a plus. Requirements - Experience with front-end frameworks, preferably React JS. - Knowledge of back-end frameworks such as Django, Flask, or Node.js. - Familiarity with database technologies such as Redshift, MySQL, or DynamoDB. - Understanding of RESTful API design and development. - Experience with version control systems such as CodeCommit or similar tools. Benefits - Compensation will be highly competitive. This is an opportunity to work on production-grade AI, advanced analytics, cloud infrastructure, enterprise data workflows, and scalable deployment systems at Revmind Labs AI. - For India-based candidates, benefits may include: - Competitive fixed compensation (up to 15 LPA) aligned to experience, ownership level, and role scope. - Remote-first work setup, with practical flexibility around working hours where delivery and collaboration needs are met. - Paid time off, sick leave, and applicable Indian public holidays. - Health insurance support or medical benefits, depending on employment structure and company policy. - Learning and upskilling support for relevant engineering, cloud, AI, product, and DevOps areas. - Home-office, equipment, or software support where applicable to the role. - Opportunity to work directly with founders and senior stakeholders on high-impact AI and analytics products. - Unusual growth opportunity as Revmind Labs AI scales its product, engineering culture, delivery standards, and GTM-stage trajectory. - A serious builder environment with high ownership, direct feedback, low ego teamwork, and meaningful technical problems.

Role Description We are seeking an AI/ML Operations Support Engineer to support enterprise machine learning and generative AI platforms in production environments. This role is focused on platform operations, production support, monitoring, deployment support, troubleshooting, automation, and governance across cloud-based AI ecosystems. This is not a pure machine learning development role. The ideal candidate will have experience supporting AI/ML solutions in production and ensuring reliability, performance, scalability, and operational readiness. Qualifications - 3-7 years of experience supporting cloud-based applications and platforms. - Experience supporting machine learning, generative AI, or advanced analytics platforms in production environments. - Strong cloud experience, preferably within AWS or similar enterprise cloud platforms. - Experience supporting APIs, integrations, and distributed systems. - Ability to work independently while collaborating effectively across teams. - Strong troubleshooting and problem-solving skills. Requirements - Experience supporting cloud-hosted applications and services in production environments. - Experience administering or supporting AI/ML platforms, model-serving environments, or generative AI solutions. - Hands-on experience supporting MLOps and/or LLMOps processes. - Experience deploying, maintaining, monitoring, and troubleshooting machine learning models in production. - Familiarity with model-serving infrastructure, inference endpoints, vector search technologies, and AI application support. - Experience with observability, model monitoring, alerting, performance management, drift detection, or governance processes. - Experience using Python or similar scripting languages for automation and operational support. - Experience supporting containerized environments and orchestration platforms. - Familiarity with CI/CD pipelines and infrastructure automation. - Understanding of machine learning lifecycle management from deployment through retirement. Benefits - Strong production support and incident management experience. - Ability to identify root causes and resolve complex technical issues. - Experience creating operational dashboards, metrics, monitoring, and alerting frameworks. - Excellent communication and collaboration skills. - Experience working with cross-functional engineering, data, and business teams. - Experience in Agile delivery environments. Key Responsibilities - Support enterprise AI/ML and generative AI platforms to ensure high availability, performance, and reliability. - Monitor production AI workloads and proactively address operational issues. - Troubleshoot model deployments, inference services, platform infrastructure, and data pipeline issues. - Participate in operational support and incident response activities. - Support model deployment, promotion, rollback, and lifecycle management processes. - Maintain version control, deployment governance, and operational standards. - Validate deployment readiness through testing and operational reviews. - Implement and maintain monitoring solutions for model health, prediction quality, latency, throughput, utilization, and operational compliance. - Create dashboards, reporting, and alerting capabilities to improve visibility and reliability. - Support governance, security, and responsible AI requirements. - Develop automation solutions to improve operational efficiency and reduce manual effort. - Support CI/CD processes and infrastructure automation initiatives. - Contribute to MLOps, LLMOps, observability, and platform reliability best practices. - Partner with Data Scientists, ML Engineers, Platform Engineers, Data Engineers, and Application Teams. - Collaborate with Cloud, Infrastructure, Security, and Architecture teams to support scalable AI solutions. - Contribute to documentation, knowledge sharing, and operational readiness efforts. Ideal Candidate The successful candidate will have a strong operations mindset, understand how AI/ML solutions function in production environments, and be comfortable troubleshooting across infrastructure, platform, application, and model layers. They should be capable of working independently, adapting to evolving technologies, and supporting multiple AI/ML platforms rather than a single vendor-specific solution.

• Help Honeycomb scale our backend systems to support our highest-volume customers. • Build organizational trust through transparent communication, giving and receiving direct and kind feedback. • Work with other backend teams to dive deep into our stack to make sure we’re getting the most out of our infrastructure. • Be trained, become, and then train others as an Incident Commander. • Help SRE and Honeycomb develop a healthy cross-Atlantic engineering culture. • Participate in the team’s on-call rotation as the EU side of a new follow-the-sun rotation. • Help the organization navigate tradeoffs between reliability and its other goals and priorities. • Optional: act as an external ambassador through blog posts, conference talks, and presentations with support from our DevRel team.

• Radiate knowledge about the service's infrastructure and reliability to the rest of the development team. • Identify parts of the system that do not scale, provide immediate palliative measures, and drive systemic resolution of contributing root cause(s). • Plan the growth of Benzinga's infrastructure. • Document every action so your learnings turn into repeatable actions and then into automation. • Improve the deployment process to make it as boring as possible. • Define, provision, and manage our production infrastructure using Kubernetes and cloud-native serverless deployed by way of Terraform. • Proactively identify and reduce security risks, in alignment with ongoing SOC2 auditing and reporting. • Develop security training and guidance to internal development teams. • Design, build and maintain core infrastructure pieces that allow Benzinga to scale, supporting thousands of concurrent users. • Be on an on-call rotation to respond to benzinga.com availability incidents and provide support for service engineers with customer incidents. • Debug production issues across all services and levels of the stack. • Make monitoring and alerting notify on symptoms and not on outages. • Manage day-to-day maintenance and evolution of Benzinga's Prometheus monitoring and alerting infrastructure. • Bundle Prometheus monitoring as an out-of-the-box monitoring solution for Benzinga products. • Build and maintain the benzinga.com public monitoring gateway. • Help migrate our current performance monitoring solution to Prometheus. • Improve coverage of Benzinga performance monitoring. • Create automated alerts to notify team members of regression.