Azure OpenAI Service for enterprise GPT and DALL-E deployment.
AWS Bedrock for foundation model deployment and management.
Google TensorFlow for building and deploying machine learning models.
Hugging Face Transformers for NLP and model hub integration.
Prompt engineering for optimizing LLM outputs and AI applications.
Conversational AI platforms for voice and text-based interactions.
LangChain framework for building LLM-powered applications.
AI safety evaluation, red teaming, and adversarial testing for enterprise AI systems.
Building and customizing AI copilots for GitHub Copilot, M365 Copilot, and enterprise workflows.
NLP for text analysis, sentiment, entity extraction, and language understanding.
AWS SageMaker for end-to-end machine learning workflows.
Google Gemini API integration for multimodal AI applications.
Deep learning for neural networks, CNNs, RNNs, and transformers.
Vector database implementation with Pinecone, Weaviate, and Chroma.
Agentic AI systems with autonomous agents and multi-agent orchestration.
OpenAI and GPT API integration for intelligent applications.
Meta PyTorch for research and production machine learning.
Retrieval-Augmented Generation for grounded AI responses with enterprise data.
Multilingual AI and NLP for Indic languages (Hindi, Tamil, Telugu) and Arabic, including translation, ASR, and regional chatbots.
AI ethics, governance, and responsible AI implementation frameworks.
Anthropic Claude API integration for safe and capable AI applications.
Large Language Models and Generative AI implementation for enterprises.
LLM and ML model fine-tuning for domain-specific applications.
Machine learning engineering for production ML systems and pipelines.
AI chatbot development for customer service and internal automation.
MLOps for machine learning operations, deployment, and monitoring.
Google Vertex AI for ML model building, training, and deployment.
Multi-agent AI orchestration with CrewAI, AutoGen, and custom frameworks.
Computer vision for image recognition, object detection, and video analysis.
Data annotation, labeling, and curation for machine learning training datasets.
