As a Lead Software Engineer, you will be at the forefront of designing and developing innovative AI-powered solutions and driving large-scale projects in Python back-end development. You will architect complex systems, lead a team of engineers, and ensure smooth integration of AI and machine learning models into back-end infrastructures. We are seeking a highly skilled Lead Software Engineer to drive the architecture and proof of concepts (POCs). In this role, you will mentor a team of engineers and act as the technical subject matter expert, solving complex programming and design challenges.
Key Responsibilities
Lead the architecture, design, and development of back-end systems using Python, with a focus on integrating AI and machine learning models into scalable applications.
Collaborate closely with data scientists to deploy, optimize, and scale AI/ML models, ensuring their seamless functionality in production environments.
Lead efforts to containerize AI-driven applications using Docker and Kubernetes, managing both development and production environments.
Use Terraform and Helm to manage cloud infrastructure, automating deployment processes and ensuring scalability on platforms like GCP and AWS.
Oversee database interactions, optimize performance, and ensure data integrity across SQL and NoSQL databases (PostgreSQL, MongoDB).
Set up monitoring (using Prometheus, Grafana) and logging frameworks (Loki, ELK stack) to ensure the reliability and security of deployed AI models and applications.
Participate in client-facing consulting projects, providing technical leadership and expertise in developing AI-powered SaaS solutions.
Participation in our consulting assignments with our clients
Development of our Software-as-a-Service products from Heka.ai
Support to Data Scientists, Data Engineers, and DevOps Engineers in projects with a strong Data component:
Back-end software development in Python: development of microservices and tools executed on the server (interaction with a database, REST API server, authentication, etc.)
Deploy and manage containerized applications using Docker and Kubernetes.