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- 10+ years in software engineering or architecture, including 5+ years leading cross-functional teams in AI/ML or distributed systems projects.
- Proven experience designing and managing large-scale backend architectures and distributed environments, with expertise in microservices, event-driven systems, and RESTful/GraphQL API development for AI/ML systems in production.
- Extensive experience with AI/ML model deployment, performance tuning, model serving, and seamless integration with backend systems.
- Deep understanding of cloud platforms (AWS, GCP, Azure), auto-scaling, serverless, and fault-tolerant architectures.
- Strong experience in machine learning frameworks (TensorFlow, PyTorch) and data pipelines for real-time processing and high-concurrency environments.
- Ability to troubleshoot and resolve complex technical challenges related to AI/ML workloads, scalability, and performance.
- Excellent leadership and communication skills, with a proven ability to mentor engineers and work closely with cross-functional stakeholders.
- Self-starter who excels at execution, balancing short-term technical delivery with long-term scalability and efficiency goals.
- Lead the execution of large-scale AI/ML projects, ensuring alignment with business objectives.
- Architect and design scalable, low-latency backend architectures, focusing on AI/ML model serving, real-time data processing, and integration with cloud infrastructure.
- Oversee deployment of AI/ML models and infrastructure, ensuring high-performance, reliability, and scalability in production environments.
- Drive decisions for cloud platforms (AWS, GCP, or Azure), auto-scaling, serverless architectures, and resource optimization.
- Influence and guide the architectural direction of the organization, ensuring future-proof, AI/ML-ready systems and solutions.
- Define benchmarks and optimize performance across AI/ML workloads.
- Lead technical troubleshooting, performance tuning, and architecture optimizations to ensure scalable and efficient deployment of AI/ML systems.
- Mentor and provide technical guidance to engineers across multiple teams, fostering a collaborative and innovative engineering culture.
- Communicate project progress, risks, and technical decisions effectively to stakeholders, ensuring transparency and alignment with business goals.
- Ensure engineering best practices are followed, including CI/CD, TDD, version control, and test automation.
AWSGraphQLLeadershipGCPKafkaKubeflowMachine LearningMLFlowPyTorchApache KafkaAzureServerlessSparkTensorflowCommunication SkillsCI/CDMicroservicesMentorship
Posted 3 months ago
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