Fullstack Machine Learning Engineer

Key Skills:

Roles and Responsibilities:

• Build end-to-end AI-powered products by designing and developing full-stack applications that integrate ML models into user experiences.
• Develop scalable APIs and services to serve ML models, manage data pipelines, and support real-time inference.
• Craft intuitive frontends using modern frameworks to visualize and interact with ML outputs.
• Operationalize ML solutions by implementing the full ML lifecycle, including data engineering, model development, deployment, monitoring, and MLOps on cloud platforms.
• Prototype rapidly to validate ideas and align with product strategy.
• Optimize performance by enhancing system and model efficiency for speed, scalability, and cost-effectiveness.
• Integrate LLMOps by designing and managing workflows for deploying, monitoring, and updating large language models in production environments.
• Collaborate cross-functionally with product managers, designers, and engineers to ensure cohesive product delivery.
• Stay ahead of the curve by keeping up with the latest in AI/ML, full-stack technologies, LLMOps, and Agentic AI to guide architectural decisions.

Skills Required:

• Strong proficiency in Python for machine learning, data processing, and API development.
• Experience with React.js (and optionally Angular) for building interactive front-end applications.
• Solid understanding of machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Hands-on experience in end-to-end ML lifecycle -- data preparation, model training, evaluation, deployment, and monitoring.
• Familiarity with LLMOps and Agentic AI concepts for large language model deployment and automation.
• Knowledge of MLOps practices, including CI/CD, containerization (Docker, Kubernetes), and monitoring.
• Experience deploying applications and models on AWS (S3, SageMaker, Lambda, EC2, etc.).
• Strong knowledge of data structures, algorithms, and system design.
• Good understanding of API development (RESTful, GraphQL) and microservices architecture.
• Working knowledge of Core Java or Java for backend services is a plus.
• Excellent problem-solving, analytical, and communication skills.

Education: