Manager - AI/ML
- Posted 4 days ago
- Be among the first 20 applicants
Job Description
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.
Key Responsibilities
Attached the Job description for your reference -
Responsibilities
Required Skills:
Bachelor's/Master's in Computer Science, AI, Data Science, or related field.
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.
Key Responsibilities
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.
Responsibilities
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.
More Info
Key Skills
API-driven design
Generative AI
Agentic AI frameworks
LLMs
cloud-native AI deployments
ADK
retrieval-augmented generation
event-driven architectures
vector databases
Pinecone
prompt engineering
LangGraph
AutoGen
Weaviate



