Position: Senior Generative AI Engineer
Experience: 5+ years
Compensation: Upto 25 LPA
Location: On-site Bengaluru, Coimbatore
Role Overview
We are seeking an accomplished Senior Generative AI Engineer with deep theoretical expertise and a proven track record in designing, developing, and deploying custom generative AI and deep learning solutions at scale. You will lead the architecture and optimization of production-grade GenAI systems, drive end-to-end MLOps strategy, and ensure robust, cost-effective, and high-performing AI deployments. This is a hands-on, impact-driven role for a technical leader passionate about innovation in AI.
Key Responsibilities
- Custom Model Development: Architect, build, and optimize advanced deep learning and generative AI models tailored to complex business needs.
- Production GenAI Systems: Design, deploy, and maintain scalable, robust, and efficient GenAI systems in production environments.
- End-to-End MLOps: Lead MLOps strategy, including system design, CI/CD pipeline development, model monitoring, and cost optimization.
- Performance Optimization: Analyze, troubleshoot, and enhance the performance of large-scale DL/GenAI models and systems.
- System Design: Oversee the design, implementation, and maintenance of production ML infrastructure, ensuring reliability and scalability.
- Collaboration: Work closely with cross-functional teams (engineering, product, data science) to deliver end-to-end AI solutions.
Required Skills & Experience
- Deep Theoretical Knowledge: Strong foundation in machine learning, deep learning, and generative AI concepts.
- Custom Model Development: Experience building and optimizing custom DL/GenAI models for production.
- Advanced Frameworks: Expert-level proficiency with TensorFlow and/or PyTorch, including distributed training techniques.
- Advanced Prompt Engineering & RAG: Mastery in prompt engineering, Retrieval-Augmented Generation, and vector database integration.
- Production Systems: Demonstrated experience designing, building, and maintaining production ML systems and CI/CD pipelines.
- Cloud & Infrastructure: Advanced experience with AWS, GCP, or Azure cloud platforms; infrastructure-as-code (Terraform); orchestration tools (Airflow, Kubeflow); and monitoring solutions.
- Cost Optimization: Proven ability to optimize system design and deployments for cost and performance.
Preferred Qualifications
- Experience with multi-cloud or hybrid cloud deployments.
- Publications or open-source contributions in GenAI or deep learning.
- Leadership in AI/ML teams or projects.
- Strong communication and mentoring skills.
Must-Have Technologies
- Frameworks: Advanced TensorFlow, PyTorch
- Distributed Training: Multi-GPU/TPU, distributed data pipelines
- Prompt Engineering: Advanced techniques, RAG systems
- Cloud: AWS, GCP, or Azure (Sagemaker, AI Services, Compute, Storage, Kubernetes)
- IaC & Orchestration: Terraform, Airflow, Kubeflow
- Monitoring: Prometheus, Grafana, or similar tools
- Version Control & CI/CD: Git, Jenkins, GitHub Actions, etc.
