MERN, Data Observability and AI Tech Lead

2 - 6 years

0 Lacs

Posted:2 days ago| Platform: Shine logo

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Job Type

Full Time

Job Description

As a Tech Lead at Seosaph-infotech, you will play a crucial role in driving the architecture, development, and integration of scalable web applications. Your expertise in MERN Stack, Data Observability, and AI/ML technologies will be key in ensuring the success of our projects. **Key Responsibilities:** - **MERN Stack Leadership**: - Lead full-stack development using MongoDB, Express.js, React.js, Node.js - Architect and implement scalable, secure, and high-performance APIs and front-end components - Guide code reviews, testing strategies, CI/CD pipeline implementations, and dev best practices - **Data Observability & Engineering**: - Implement and manage data observability platforms (e.g., Azure Data Service, and other tools through APIs and Databases) - Build robust data monitoring pipelines for detecting anomalies, schema drift, and data downtime - Collaborate with data engineers and analysts to ensure trust and quality in data pipelines - **AI & Machine Learning Integration**: - Oversee the integration of AI/ML features into web applications (e.g., recommendation systems, NLP) - Collaborate with data scientists to deploy, monitor, and scale ML models in production - Lead AI proof-of-concepts, model evaluation, and MLOps adoption (e.g., MLflow, Kubeflow) - Chatbot, GenAI implementation experience would be add on - **Team & Project Leadership**: - Lead cross-functional agile teams and manage sprint planning, code delivery, and technical backlogs - Mentor junior developers and promote a culture of engineering excellence - Translate business requirements into technical specs and delivery plans **Qualifications Required:** - 6+ years of experience with the MERN stack and modern JavaScript/TypeScript - 2+ years leading engineering teams or projects - Experience with data observability tools and monitoring strategies for data pipelines - Proficient in AI/ML concepts and integrating ML models into real-time systems - Strong foundation in cloud infrastructure (Azure, etc) - Familiarity with containerization (Docker/Kubernetes), GitOps, and CI/CD workflows,

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