Manager - AI/ML

Aimlroles; Bengaluru; On-site; INR 1,400,000 - 3,200,000; Full time

Job summary

KPMG India is seeking an AI Engineer to design, develop, and deploy advanced AI models and generative AI solutions. You will build end-to-end AI systems, work with LLMs, RAG pipelines, and vector databases, and ensure production readiness with scalable APIs and microservices.

The role requires hands-on experience across cloud platforms and NLP techniques, with collaboration acrossSolution Architects and business teams to deliver responsible, compliant AI solutions in a dynamic environment.

Qualifications

• 3-9 years of experience in AI/ML development and deployment.
• Hands-on experience with LLMs and Generative AI applications.
• Proficiency in Python, SQL, and ML frameworks (TensorFlow, PyTorch).
• Strong understanding of Generative AI concepts (transformers, embeddings, fine-tuning).
• Experience with Agentic AI frameworks (ADK, LangGraph, AutoGen).
• Familiarity with cloud platforms (AWS, Azure, GCP) for AI deployment.
• Knowledge of prompt engineering, vector databases (Pinecone, Weaviate), and retrieval-augmented generation (RAG).
• Hands-on with cloud-native AI deployments (Azure, AWS, GCP).
• Familiarity with API-driven design, microservices, and event-driven architectures

Responsibilities

• Design, build, and fine-tune machine learning, deep learning, and Generative AI models for real-world use cases.
• Develop Agentic AI systems using frameworks such as ADK, LangGraph, or equivalent for orchestration and reasoning.
• Build solutions leveraging LLMs, RAG pipelines, embeddings, and vector databases.
• Design and implement AI systems for text, structured/unstructured data, and conversational use cases.
• Apply core NLP techniques including classification, summarization, entity recognition, and semantic search.
• Develop scalable algorithms for search, retrieval, and real-time inference.
• Build and integrate AI microservices into enterprise systems using APIs, event-driven architectures, and cloud services.
• Deploy and operate AI workloads using Docker, Kubernetes, and managed cloud AI platforms (Azure ML, AWS SageMaker, GCP Vertex AI).
• Optimize solutions for performance, cost, latency, and security.
• Collect, preprocess, and manage large datasets for fine-tuning and inference.
• Continuously monitor and optimize models and prompts for accuracy, scalability, and efficiency.
• Partner with Solution Architects and business teams to translate requirements into production-ready AI solutions.
• Champion Responsible AI practices, including fairness, bias mitigation, and compliance.

Skills

Python

SQL

ML frameworks

LLMs

Generative AI

Cloud platforms

Prompt engineering

Vector databases

RAG

API-driven design

Microservices

Education

Bachelor's/Master's in Computer Science, AI, Data Science

Tools

TensorFlow

PyTorch

ADK

LangGraph

AutoGen

Pinecone

Weaviate

Azure ML

AWS SageMaker

GCP Vertex AI

Job description

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.

AI Engineer

Role Summary

An AI Engineer designs, develops, and deploys AI models and solutions, including Generative AI applications, to solve real-world business problems. This role involves hands-on coding, model training, and integration into production systems.

Attached the Job description for your reference -

• Design, build, and fine-tune machine learning, deep learning, and Generative AI models for real-world use cases.
• Develop Agentic AI systems using frameworks such as ADK, LangGraph, or equivalent for orchestration and reasoning.
• Build solutions leveraging LLMs, RAG pipelines, embeddings, and vector databases.
• Design and implement AI systems for text, structured/unstructured data, and conversational use cases.
• Apply core NLP techniques including classification, summarization, entity recognition, and semantic search.
• Develop scalable algorithms for search, retrieval, and real-time inference.
• Build and integrate AI microservices into enterprise systems using APIs, event-driven architectures, and cloud services.
• Deploy and operate AI workloads using Docker, Kubernetes, and managed cloud AI platforms (Azure ML, AWS SageMaker, GCP Vertex AI).
• Optimize solutions for performance, cost, latency, and security.
• Collect, preprocess, and manage large datasets for fine-tuning and inference.
• Continuously monitor and optimize models and prompts for accuracy, scalability, and efficiency.
• Partner with Solution Architects and business teams to translate requirements into production-ready AI solutions.
• Champion Responsible AI practices, including fairness, bias mitigation, and compliance.

KPMG India has a policy of providing equal opportunity for all applicants and employees regardless of their color, caste, religion, age, sex/gender, national origin, citizenship, sexual orientation, gender identity or expression, disability or other legally protected status. KPMG India values diversity and we request you to submit the details below to support us in our endeavor for diversity. Providing the below information is voluntary and refusal to submit such information will not be prejudicial to you.

Required Skills:

• 3-9 years of experience in AI/ML development and deployment.
• Hands-on experience with LLMs and Generative AI applications.
• Proficiency in Python, SQL, and ML frameworks (TensorFlow, PyTorch).
• Strong understanding of Generative AI concepts (transformers, embeddings, fine-tuning).
• Experience with Agentic AI frameworks (ADK, LangGraph, AutoGen).
• Familiarity with cloud platforms (AWS, Azure, GCP) for AI deployment.
• Knowledge of prompt engineering, vector databases (Pinecone, Weaviate), and retrieval-augmented generation (RAG).
• Hands-on with cloud-native AI deployments (Azure, AWS, GCP).
• Familiarity with API-driven design, microservices, and event-driven architectures

Bachelor's/Master's in Computer Science, AI, Data Science, or related field.