About KPMG in India

KPMG entities in India are professional services firm(s). These Indian member firms are affiliated with KPMG International Limited. KPMG was established in India in August 1993. Our professionals leverage the global network of firms, and are conversant with local laws, regulations, markets and competition. KPMG has offices across India in Ahmedabad, Bengaluru, Chandigarh, Chennai, Gurugram, Jaipur, Hyderabad, Jaipur, Kochi, Kolkata, Mumbai, Noida, Pune, Vadodara and Vijayawada.

KPMG entities in India offer services to national and international clients in India across sectors. We strive to provide rapid, performance-based, industry-focused and technology-enabled services, which reflect a shared knowledge of global and local industries and our experience of the Indian business environment

KPMG India Services is seeking a highly skilled Generative AI Engineer to join our delivery team. The Generative AI Engineer will play a critical role in designing, building, and deploying enterprise‑grade AI solutions using modern cloud and data platforms such as Databricks, Microsoft Fabric (AI Fabric), Azure, and Snowflake.

This role requires strong hands‑on engineering capability combined with a solid understanding of AI/ML frameworks and scalable cloud architectures to deliver measurable business value.

Qualifications

Education

• Bachelor’s or master’s degree in computer science, Engineering, Data Science, or a related field.

• Relevant certifications in AI/ML, Azure, cloud computing, or data engineering are a strong plus.

Experience

• 3+ years of hands‑on experience in Generative AI, Machine Learning, or advanced data engineering roles.

• Demonstrated experience building and deploying AI/ML solutions using frameworks such as TensorFlow, PyTorch, or equivalent.

• Hands‑on experience with cloud platforms such as AWS/Azure/GCP.

Skills

• Strong proficiency in Python, JavaScript, or similar programming languages.

• Solid understanding of AI/ML algorithms, model deployment patterns, and MLOps practices.

• Expertise in data engineering, distributed processing, and cloud‑based architecture.

• Strong analytical and problem‑solving skills with the ability to work effectively in cross‑functional, global teams.

• Excellent communication skills, with the ability to translate complex technical concepts for both technical and non‑technical stakeholders

Key Responsibilities

1. AI Solution Design & Development

• Architect, design, and deliver Generative AI and machine learning solutions to address complex business challenges across KPMG service lines.

• Develop end-to-end AI solutions incorporating robust data engineering and model development practices, leveraging industry-standard tools for orchestration, deployment, and scalable, governed data management.

• Utilize leading cloud services to build secure, resilient, and highly scalable AI solutions.

2. Technical Leadership & Collaboration

• Work closely with data scientists, AI architects, platform engineers, and business stakeholders to define solution designs and technical requirements.

• Drive AI initiatives from experimentation and proof‑of‑concept through to enterprise‑scale production deployments.

• Collaborate with cross‑functional teams to embed AI capabilities into existing platforms, workflows, and business processes.

3. Engineering & Implementation

• Write clean, efficient, and maintainable code using Python, JavaScript, or similar languages, with a focus on AI/ML and distributed data processing.

• Build and optimize scalable data pipelines to support data ingestion, feature engineering, model training, and inference.

• Implement model lifecycle management, deployment automation, and monitoring using industry-standard tools and cloud-native services.

4. Cloud & Platform Operations

• Deploy, manage, and operate AI solutions on Cloud, adhering to cloud‑native best practices for security and reliability.

• Design and maintain CI/CD pipelines to enable automated model deployment and continuous improvement.

• Continuously optimize cloud infrastructure and AI workloads for performance, scalability, and cost efficiency.

5. Performance Optimization & Reliability

• Monitor and tune AI models, data pipelines, and infrastructure to ensure optimal performance and scalability.

• Apply model fine‑tuning, hyperparameter optimization, and feature engineering techniques to improve solution accuracy and effectiveness.

• Diagnose and resolve production issues related to data processing, model behavior, and cloud infrastructure.

7. Innovation & Continuous Improvement

• Stay up to date with advancements in Generative AI, large language models (LLMs), cloud platforms, and big‑data ecosystems.

• Explore and evaluate emerging tools, frameworks, and architectures to enhance solution quality and delivery speed.

• Contribute to engineering standards, reusable assets, and technical documentation to support consistency and quality across KPMG India Services.