The ideal candidate is a self-motivated, multi-tasker, and demonstrated team-player. You will be a responsible for the development of new software products and enhancements to existing products. You should excel in working with large-scale applications and frameworks and have outstanding communication and leadership skills.
Key Responsibilities
- Design and build scalable end-to-end enterprise systems, bridging advanced frontends, mobile applications, and high-performance async backends.
- Deploy, manage, and optimize the production inferencing of large machine learning models (TensorFlow, PyTorch) and Large Language Models (LLMs) within Agentic AI architectures.
- Define robust GraphQL integration contracts to seamlessly connect AI outputs with core enterprise services.
- Translate ambiguous business needs into concrete technical architectures and actionable engineering tasks.
- Lead cross-functional engineering teams through the full delivery lifecycle to drive measurable business ROI.
- Manage stakeholder expectations regarding realistic AI capabilities, mitigating risks, and defining strict project boundaries.
- Optimize system performance across compute, memory, and network layers, including low-latency LLM inference and cost-efficient resource utilization (GPU/CPU).
- Design secure systems with robust authentication/authorization (OAuth, RBAC), data privacy safeguards, and defenses against AI-specific threats such as prompt injection and data exfiltration.
- Design and build scalable, event-driven systems that can handle high traffic reliably, using best practices to ensure data consistency and safe processing (e.g., avoiding duplicate or lost events).
Technical Requirements
- Design: Strong foundation in system analysis and design, including object-oriented programming (OOP), class design, and the ability to create clear system diagrams (e.g., class diagrams, architecture diagrams).
- Frontend: Advanced React proficiency, specifically for building interactive datatables, complex dashboards, and graph-based interfaces (e.g., React Flow).
- Mobile: Hands-on cross-platform mobile development experience (React Native is a must; Flutter is acceptable).
- Backend: Deep expertise in robust asynchronous backend frameworks using Python or Go.
- API: Extensive experience designing, implementing, and consuming GraphQL APIs.
- Testing: Experience with automated testing is required, including UI testing tools such as Selenium (or equivalent), along with unit and integration testing.
- AI/ML Integration: Proven track record of serving and inferencing large-scale ML models and LLMs (Agentic AI workflows) in production environments.
- Data Storage: Mastery of advanced data storage technologies, specifically NoSQL, Cassandra, or equivalent columnar databases.
- Infrastructure & Streaming: Strong proficiency with Kubernetes (or equivalent orchestration) and Apache Kafka (or equivalent message brokers/event streaming).
- Systems Architecture: Solid network-level knowledge for designing secure, scalable, and low-latency system communications.
