Gen AI - Consultant

Aimlroles; Bengaluru; On-site; INR 2,400,000 - 4,200,000; Full time

Job summary

KPMG in India is seeking a Senior AI Engineer to design and implement advanced AI and GenAI solutions integrated into enterprise platforms. The role requires a strong CS foundation, NLP expertise, and cloud-native deployment experience.

You will work with LLMs, RAG pipelines, embeddings, and vector databases, building scalable AI systems and mentoring engineers while ensuring responsible AI practices across solutions.

Qualifications

• Strong foundation in computer science, applied mathematics, and NLP for building AI systems.
• Experience deploying AI/GenAI on cloud-native infrastructures.
• Hands-on with vector databases, embeddings, and RAG pipelines.

Responsibilities

• Design and implement AI/ML models for text and multimodal data.
• Develop and optimize NLP pipelines and semantic search capabilities.
• Integrate AI microservices into enterprise platforms with CI/CD and monitoring.

Skills

Python

NLP & LLMs

TensorFlow/PyTorch

MLOps

Cloud deployment

Tools

Spark

Airflow

SQL/NoSQL

FAISS/Pinecone

Docker/Kubernetes

LangChain

Job description

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.

We are seeking a Senior AI Engineer to design and implement advanced AI and GenAI solutions that integrate seamlessly into enterprise platforms. This role requires a strong foundation in computer science, applied mathematics, and natural language processing (NLP), coupled with experience in deploying scalable AI systems on cloud-native infrastructures.

AI & NLP Development

• Design and implement models for text, structured/unstructured data, and conversational AI systems.
• Work with large language models (LLMs), RAG pipelines, embeddings, and vector databases.
• Apply core NLP techniques: tokenization, entity recognition, text classification, summarization, and semantic search.

Mathematical & Algorithmic Foundations

• Apply fundamentals of linear algebra, probability, optimization, and statistics to AI model design and performance tuning.
• Develop scalable algorithms for search, retrieval, and real-time inference.

System Integration & Deployment

• Build and integrate AI microservices into enterprise-grade systems via APIs, event-driven patterns, and cloud services.
• Implement MLOps workflows including CI/CD, model monitoring, retraining pipelines, and explainability.

Cloud & Scalability

• Deploy and optimize AI workloads on Azure ML, AWS Sagemaker, or GCP Vertex AI.
• Ensure cost optimization, latency reduction, and security compliance.

Leadership & Collaboration

• Mentor AI Engineers and guide technical decision-making.
• Partner with Solution Architects and business teams to align models with client requirements.
• Champion Responsible AI principles (fairness, bias detection, compliance).

Key Skills

• Strong programming skills in Python (PyTorch, TensorFlow, Hugging Face, LangChain).
• Solid grasp of mathematics for AI: linear algebra, probability, optimization, statistics.
• Expertise in NLP & LLMs: embeddings, transformers, semantic search, RAG, prompt engineering.
• Experience with data engineering tools (Spark, Airflow, SQL/NoSQL DBs).
• Proficiency in vector databases (FAISS, Pinecone, Weaviate) and knowledge graphs.
• Hands-on with cloud-native AI deployments (Azure, AWS, GCP).
• Familiarity with API-driven design, microservices, and event-driven architectures.
• Exposure to DevOps/MLOps practices including containerization (Docker/Kubernetes).