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.