Full Stack Developer - AI/ML

Job Description : Role Overview :

Were looking for a Senior Full Stack Developer with deep expertise in Machine Learning (ML), Artificial Intelligence (AI), and data-centric application development. In this role, youll architect and build scalable, data-driven applications, integrating ML/AI models seamlessly across front-end and back-end systems. Youll be instrumental in leading full-cycle development, mentoring teams, and driving intelligent solutions that turn large datasets into meaningful, actionable insights.

Key Responsibilities :

• Full Stack Development : Design, develop, and maintain robust, scalable data applications using modern front-end and back-end technologies.

• Data Application Engineering : Build and optimize data pipelines and processing frameworks for handling large-scale datasets.

• AI/ML Integration : Collaborate with data scientists to deploy and integrate ML/AI models into production-ready applications.

• Technical Leadership : Drive architectural decisions, enforce development best practices, and ensure code quality.

• Mentorship : Guide and support junior engineers through reviews and technical discussions.

• Collaboration : Partner with cross-functional teams product, data, and design to translate ideas into powerful, user-focused applications.

• Optimization & Troubleshooting : Diagnose and resolve performance issues across the stack.

• Documentation : Maintain clear, detailed technical documentation for scalability and knowledge sharing.

Required Skills & Experience :

• Education : B.E./B.Tech/M.E./M.Tech in Computer Science, Data Science, or a related discipline.

• Experience : 5+ years in software development (including 2+ years in a senior/lead role).

• Full Stack Proficiency : Strong in front-end (React, Angular, or Vue.js) and back-end (Node.js, Python, Java) development.

• Data Engineering : Skilled in data processing frameworks (Apache Spark, Kafka) and building ETL pipelines.

• ML/AI Expertise : Hands-on experience with TensorFlow, PyTorch, or scikit-learn, and deploying ML models in production.

• Databases : Proficiency in SQL and NoSQL systems (PostgreSQL, MongoDB) and data warehousing tools (Snowflake, BigQuery).

• Cloud & DevOps : Experience with AWS, GCP, or Azure, and CI/CD, Docker, Kubernetes, MLflow, or SageMaker.

• Visualization : Familiarity with D3.js, Tableau, or Power BI for data visualization.

Soft Skills :

• Excellent communication and collaboration skills.