Senior GenAI Engineer

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.