Associate Consultant

As an AI Software Engineer in KDN’s AI Labs, you'll step into the real-world application of artificial intelligence and become part of a team that's pushing the boundaries of what's possible in professional services. You'll dive straight into supporting the development of cutting-edge AI solutions that solve real-world challenges across service lines. This isn't about watching from the sidelines – you'll work shoulder-to-shoulder with experienced AI engineers and researchers, transforming use cases and ideas into powerful solutions while building your foundation in professional AI development.

AI Research and Development

• Conduct applied research in AI Agents, Traditional AI (Knowledge-based AI, Symbolic AI), and Generative AI (Deep Learning, RNN, CNN).
• Develop and optimize state-of-the-art AI models for real-world applications across various business domains.
• Work on LLMs, reinforcement learning, and multi-agent systems for intelligent automation.
• Contribute to the advancement of AI-driven decision-making systems and explainable AI (XAI).

AI Model Development and Deployment

• Design, train, and fine-tune deep learning models for computer vision, NLP, and multimodal AI.
• Implement AI-driven software applications using cutting-edge frameworks like TensorFlow, PyTorch, JAX, and Hugging Face.
• Develop AI pipelines that integrate symbolic AI with deep learning for enhanced interpretability and robustness.
• Collaborate with data engineers to build scalable AI models and deploy them on cloud and on-prem infrastructure.

Experimentation and Benchmarking

• Conduct experiments to evaluate AI models for accuracy, robustness, and efficiency.
• Optimize model performance, interpretability, and generalization to new datasets.
• Benchmark AI models against industry-standard datasets and propose improvements.

Cross-functional Collaboration

• Work closely with AI engineers, data scientists, and business stakeholders to translate AI research into enterprise-grade solutions.
• Contribute to AI whitepapers, patents, and publications in top-tier conferences and journals.
• Stay updated with the latest AI trends and drive innovation in AI-driven automation and enterprise intelligence.

Education

• M.Tech in Computer Science, AI, Data Science, Machine Learning, or related fields.
• Strong foundation in mathematics, statistics, and optimization techniques for AI model development.

Technical Skills

• Deep Learning: Proficiency in CNNs, RNNs, Transformers, GANs, VAEs, Diffusion Models.
• AI Agents: Hands-on experience with reinforcement learning, multi-agent systems, and intelligent automation.
• Traditional AI: Experience with rule-based systems, expert systems, and hybrid AI approaches.
• Programming: Strong skills in Python (TensorFlow, PyTorch, Hugging Face), C++, and/or Julia.
• Data Processing: Proficiency in Numpy, Pandas, SQL, Spark, and data augmentation techniques.
• AI Frameworks: Hands-on experience with LLMs (GPT, BERT, LLaMA, Claude, Mistral), OpenAI API, LangChain, and Retrieval-Augmented Generation (RAG).
• Optimization: Experience with hyperparameter tuning, model pruning, and quantization techniques.
• Cloud & DevOps: Familiarity with AWS, Azure, GCP, Docker, Kubernetes, MLflow, and CI/CD pipelines for AI deployment.

Preferred Experience

• Published research papers in AI/ML conferences (Neur-IPS, ICML, CVPR, AAAI, ACL, etc.).
• Experience in fine-tuning and optimizing Large Language Models (LLMs) for enterprise use cases.
• Knowledge of neuro-symbolic AI, causal reasoning, and graph neural networks (GNNs).
• Exposure to quantum computing, federated learning, and edge AI is a plus.

Experience Level

Mid Level