Key Responsibilities:
AI System Design & Development:
- Architect, develop, and deploy large-scale Generative AI, LLM-based systems, including intelligent agents and automation workflows.
LLM Integration & Optimization:
- Integrate and optimize large language models for reasoning, summarization, and structured content generation.
- Apply prompt design, fine-tuning, and evaluation strategies to ensure reliable and domain-aware outputs.
Knowledge & Retrieval Systems:
- Design and implement retrieval-augmented and context-aware AI pipelines, combining embeddings, semantic search, and hybrid retrieval methods.
Backend Engineering:
- Build robust, scalable backend services and APIs using TypeScript and Python, including real-time communication and data streaming capabilities.
- Ensure high performance, fault tolerance, and clean integration between AI components and backend systems.
Data Pipelines & Processing:
- Develop and manage pipelines to extract, process, and transform unstructured data (code, documents, text) into AI-ready formats.
Infrastructure & Deployment:
- Design and maintain cloud-native, containerized, and event-driven architectures with Infrastructure-as-Code (IaC) practices.
- Collaborate with DevOps teams to implement CI/CD, observability, and environment automation.
Model Evaluation & Monitoring:
- Establish model evaluation metrics, continuous validation workflows, and performance dashboards to ensure production reliability and drift detection.
Leadership & Mentorship:
- Lead design discussions, perform technical reviews, and mentor AI engineers.
- Collaborate cross-functionally with product and platform teams to translate AI capabilities into production-grade solutions.
Required Skills
- Strong proficiency in Python and TypeScript, with a solid background in backend or API development.
- Proven experience in LLM-based application design, Generative AI workflows, or AI agent systems.
- Understanding of retrieval-augmented generation (RAG) and semantic/embedding-based search principles.
- Experience building scalable cloud-native services, including event-driven or asynchronous architectures.
- Familiarity with infrastructure automation, container orchestration, and CI/CD pipelines.
- Exposure to MLOps principles — model lifecycle management, evaluation, and continuous improvement.
- Strong problem-solving, analytical, and architectural reasoning skills.
- Excellent communication, collaboration, and mentoring abilities.
Preferred Qualifications & Experiences:
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or related disciplines.
- 3+ years of professional experience in AI/ML software engineering, including 2+ years in Generative AI or LLM-based systems.
- Previous experience leading small engineering teams or driving architectural decisions is highly preferred.
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