AIML Engineer

KPMG is one of the world’s leading professional services firms and the fastest growing Big Four accounting firm

About The Job:

• Required Experience: 6-9 years
• We are seeking a highly skilledAI/ML Engineer with 6–9 years of experience in designing, developing, deploying, and optimizing Machine Learning and Generative AI solutions.
• The ideal candidate will have strong expertise in machine learning, deep learning, NLP, MLOps, cloud platforms, and AI solution architecture.
• The role requires close collaboration with business stakeholders, data engineers, product teams, and solution architects to deliver scalable AI-driven solutions.

Essential Job Functions:
AI/ML Solution Development

• Design, develop, and deploy Machine Learning and Deep Learning models for business use cases.
• Build predictive analytics, recommendation systems, classification, clustering, forecasting, and optimization models.
• Develop and maintain end-to-end AI/ML pipelines from data ingestion to model deployment.
• Evaluate and improve model performance using appropriate metrics and validation techniques.

Generative AI & LLMs

• Design and implement solutions using Large Language Models (LLMs) such as GPT, Llama, Gemini, Claude, etc.
• Build Retrieval-Augmented Generation (RAG) frameworks for enterprise knowledge management and conversational AI.
• Fine-tune, optimize, and deploy foundation models for specific business use cases.
• Develop AI-powered chatbots, virtual assistants, and intelligent automation solutions.

Data Engineering & Feature Engineering

• Work with structured and unstructured datasets from multiple sources.
• Perform data preprocessing, feature engineering, data cleansing, and transformation.
• Collaborate with Data Engineering teams for scalable data pipelines.
• Ensure data quality, governance, and compliance standards are met.

MLOps & Deployment

• Implement CI/CD pipelines for ML model deployment and monitoring.
• Manage model lifecycle including versioning, retraining, testing, and production deployment.
• Deploy AI solutions using Docker, Kubernetes, and cloud-native services.
• Monitor model drift, performance degradation, and operational metrics.

Stakeholder Management

• Collaborate with business teams to understand requirements and translate them into AI use cases.
• Present technical solutions, findings, and recommendations to stakeholders.
• Mentor junior AI/ML engineers and contribute to best practices.

Qualifications:
Required Technical Skills

Machine Learning & AI

• Strong expertise in:
  • Supervised and Unsupervised Learning
  • Deep Learning
  • Ensemble Methods
  • Reinforcement Learning (Good to Have)
  • Time Series Forecasting
  • Recommendation Systems

Generative AI

• Prompt Engineering
• RAG Architecture
• Vector Databases (Pinecone, Weaviate, ChromaDB, FAISS)
• LLM Fine-Tuning
• AI Agent Frameworks
• LangChain, LlamaIndex, Semantic Kernel

Programming Languages

• Python (Mandatory)
• SQL
• PySpark (Preferred)

Frameworks & Libraries

• TensorFlow
• PyTorch
• Scikit-Learn
• Keras
• Hugging Face Transformers
• OpenAI APIs
• LangChain

Cloud Platforms

• Azure AI Services
• Azure OpenAI
• AWS SageMaker
• Google Vertex AI

MLOps Tools

• MLflow
• Kubeflow
• TensorFlow Serving
• Docker
• Kubernetes
• GitHub Actions / Azure DevOps

Data Technologies

• SQL Server
• PostgreSQL
• MongoDB
• Databricks
• Snowflake

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
• 6–9 years of experience in AI/ML engineering and solution development.
• Hands-on experience delivering AI/ML projects in production environments.
• Strong understanding of software engineering principles and scalable system design.
• Experience working in Agile/Scrum environments.

Preferred Skills

• Experience with Agentic AI frameworks.
• Knowledge of Responsible AI and AI Governance.
• Experience in Computer Vision projects.
• Exposure to Graph Neural Networks and Knowledge Graphs.
• Azure AI Engineer Associate, AWS ML Specialty, or equivalent certifications.
• Experience in healthcare, financial services, retail, or enterprise digital transformation projects.