Senior Generative AI Engineer (5+ Years Experience)

r3 Consultant; Coimbatore District; On-site; INR 2,250,000 - 2,750,000; Full time

Job summary

A technology consulting firm is seeking a Senior Generative AI Engineer with over 5 years of experience in deep learning and generative AI. This role focuses on the architecture and optimization of GenAI systems, implementing end-to-end MLOps strategies, and working closely with cross-functional teams. Ideal candidates will demonstrate proficiency in TensorFlow or PyTorch and a strong foundation in cloud systems, including AWS or Azure. This hands-on position offers a competitive compensation package and opportunities for professional growth.

Qualifications

• 5+ years of experience in generative AI and deep learning.
• Experience in building and optimizing custom DL/GenAI models.
• Strong foundation in cloud platforms and infrastructure-as-code.

Responsibilities

• Architect and optimize generative AI models tailored to business needs.
• Design and maintain scalable production GenAI systems.
• Lead end-to-end MLOps strategy for AI solutions.

Skills

Machine learning

Deep learning

Generative AI

TensorFlow

PyTorch

Prompt engineering

AWS

GCP

Azure

Terraform

Tools

Git

Jenkins

Prometheus

Grafana

Airflow

Kubeflow

Job description

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.