Lead AI Engineer
20 hours ago
This role is for one of the Weekday's clients
Salary range: Rs Rs ie INR 12-18 LPA)
Min Experience: 4 years
Location: Bangalore
JobType: full-time
As a Backend-Focused AI Systems Engineer, you will design, build, and scale production-grade AI platforms that seamlessly integrate advanced machine learning models into real-world applications. This role centers on strong backend engineering, system architecture, and reliable deployment of AI solutions across cloud, hybrid, and edge environments. You will own critical components of the AI lifecycle—from data pipelines and model serving to monitoring and optimization—while collaborating closely with data, DevOps, and infrastructure teams to deliver robust, high-performance systems that meet enterprise standards.
Key Responsibilities
- Design and lead the development of scalable, resilient AI systems with a strong emphasis on backend architecture.
- Build and manage modular AI pipelines covering data preprocessing, model training, deployment, and monitoring.
- Develop and maintain APIs, microservices, and backend services to integrate AI models into products and real-time systems.
- Collaborate with DevOps and infrastructure teams to deploy solutions using containers, CI/CD pipelines, and cloud or edge platforms.
- Ensure high code quality, maintainability, and reliability through version control, automated testing, and best engineering practices.
- Optimize system performance and scalability, including hardware-aware deployments for GPUs or edge devices.
- Implement standards for model reproducibility, governance, observability, and lifecycle management.
- Mentor junior engineers and contribute to a culture of engineering excellence and scalable system design.
What Makes You a Great Fit
- Strong backend development experience in Python, with exposure to modern frameworks such as FastAPI, Flask, or gRPC.
- Solid understanding of AI/ML system architecture and end-to-end model lifecycle management.
- Hands-on experience with Docker, Kubernetes, and CI/CD pipelines in production environments.
- Knowledge of distributed systems, asynchronous processing, and well-designed APIs.
- Familiarity with model tracking and versioning tools such as MLflow, DVC, or similar.
- Comfortable working with Linux-based systems and containerized deployments.
- A problem-solver who enjoys building reliable systems at scale and collaborating across teams.
- A growth-oriented engineer with a passion for translating AI research into dependable, real-world solutions.
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