AI Engineer

Lead the full lifecycle of Generative AI solutions — from design and development to deployment — encompassing LLM-powered workflows, RAG pipelines, OCR, and Agentic AI systems.

Architect and implement secure, scalable AI infrastructures using GCP, AWS, or Azure.

Apply LLMOps best practices, including fine-tuning, advanced prompt engineering, and model optimization, to maximize performance and contextual accuracy.

Design and maintain APIs and microservices (FastAPI, REST, Spring Boot) to integrate AI capabilities into enterprise systems.

Build and optimize data pipelines and manage vector databases and Elasticsearch for efficient knowledge retrieval and decision-making.

Implement MLOps, CI/CD, and DevOps pipelines using Kubernetes, Docker, Jenkins, and Ansible for automated deployment and monitoring.

Ensure system reliability and observability through logging, monitoring, and infrastructure-as-code practices (e.g., ELK stack).

Collaborate cross-functionally with product, engineering, and business teams to align AI solutions with organizational goals.

Mentor and guide junior engineers, setting best practices for scalable, maintainable AI development.