Responsibilities
- Execute software engineering tasks to support end-to-end machine learning operations, with a focus on scaling workflows for large data and distributed systems.
- Design, build, and maintain MLOps systems, including microservices, queuing systems, APIs, and orchestration workflows using Python, Kubernetes, Kafka, and modern database systems.
- Implement observability tools such as Prometheus and Grafana to ensure reliability, performance, and visibility of ML systems in production.
- Collaborate closely with data scientists and machine learning engineers to streamline workflows for LLM, generative AI, and Agentic AI development and deployment.
- Review architecture and implementation plans to ensure alignment with organizational goals, scalability, and best practices.
- Mentor junior and mid-level engineers, fostering a culture of collaboration, innovation, and operational excellence.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field.
- 5+ years of professional experience in Machine Learning, MLOps, DevOps, or Software Engineering, with a focus on building scalable and reliable software systems.
- Proficiency in Python and Linux, with strong knowledge of designing scalable, distributed systems.
- Hands-on experience in designing, implementing, and maintaining MLOps workflows, including CI/CD pipelines, monitoring, and production optimization.
- Strong background in cloud computing (AWS/GCP), infrastructure as code (Terraform), containerization, and orchestration (Kubernetes).
- Solid understanding of modern software development practices such as test-driven development (TDD), systems thinking, and CI/CD automation.
- Experience deploying and managing observability tools such as Prometheus, Grafana, and OpenTelemetry to ensure high reliability and performance in production ML systems.
- Expertise in scaling and optimizing distributed systems for large-scale, multi-node computations.
- Proven ability to collaborate effectively with cross-functional teams, supporting data scientists and ML engineers in deploying and scaling ML models, including LLMs and generative AI systems.
- Demonstrated capability in leading technical projects, mentoring junior and mid-level engineers, and fostering a culture of collaboration, innovation, and operational excellence.
- Pragmatic approach: delivering efficient, real-world solutions while maintaining technical excellence.
- Strong attention to detail with a focus on precision and robustness in system design and implementation.
- Accountability for deliverables, adherence to company standards, and commitment to ethical guidelines.
