Staff GenAI Engineer

Role Overview

• Lead the architecture, design, and development of scalable backend systems integrating machine learning and Generative AI models into production-grade applications.
• Drive end-to-end delivery of GenAI solutions, including LLM-based systems, RAG architectures, and agentic workflows, from experimentation to production.
• Design and implement agentic AI systems enabling multi-step reasoning, tool calling, memory management, and orchestration.
• Apply a strong understanding of machine learning techniques (regression, classification, clustering, decision trees, neural networks, probabilistic models) to solution design and implementation.
• Collaborate closely with data scientists to train, fine-tune, evaluate, and deploy ML/AI models, ensuring reliability and performance at scale.
• Work with structured and unstructured data sources, designing efficient data pipelines and storage strategies.
• Lead development of backend microservices and APIs supporting AI-driven applications.
• Ensure production readiness of AI systems, including performance optimization, monitoring, cost control, and security.
• Define cloud-native deployment strategies for AI solutions on AWS, Azure, and GCP.
• Establish containerized deployment standards using Docker and Kubernetes across development and production environments.
• Oversee data architecture, performance, and integrity across SQL and NoSQL databases (PostgreSQL, MongoDB).
• Implement monitoring and logging frameworks to ensure reliability, observability, and operational excellence of AI systems.

Requirements

• 8+ years of experience in software engineering, with strong backend expertise in Python and GenAI Exposure.
• Proven experience delivering AI/ML or Generative AI systems in production environments.
• Familiarity with gen Ai frameworks like Lang chain, Google ADK and other Agent development Kits.
• Strong knowledge of cloud-native architectures, microservices, and distributed systems.
• Hands-on expertise with Docker, Kubernetes, CI/CD pipelines, and infrastructure-as-code (Terraform).
• Experience with PostgreSQL, MongoDB, or equivalent databases.
• Ability to operate in client-facing environments and communicate complex technical concepts clearly.
• Experience mentoring or leading technical teams is preferred.

Tech Stack

• AWS
• Azure
• Cloud
• Distributed Systems
• Docker
• Google Cloud Platform
• Kubernetes
• Microservices
• MongoDB
• NoSQL
• Postgres
• Python
• SQL
• Terraform

Benefits

• Opportunity to lead cutting-edge AI projects in a global consulting environment.
• Leadership development programs and training sessions at our global centers.
• A dynamic and collaborative team environment with diverse projects.