- Building and fine-tuning large language models (LLMs) and implementing RAG, prompt engineering, and multi-agent AI workflows using Autogen, CrewAI, and LangGraph.
- Developing and deploying end-to-end AI/ML pipelines, APIs, and microservices using FastAPI, Docker, Kubernetes, and cloud-native patterns on AWS & IBM Cloud.
- Automating AI/ML workflows with CI/CD pipelines using Jenkins, Git, and infrastructure-as-code via AWS CloudFormation.
- Designing and orchestrating agentic AI workflows with human-in-the-loop (HITL), memory, delegation, and hierarchical task management.
- Building real-time, event-driven AI systems with asynchronous Python, WebSockets, message queues (RabbitMQ), and background workers.
- Implementing OCR-driven document intelligence pipelines for structured and semi-structured data extraction using GPT-based vision models.
- Architecting scalable, fault-tolerant AI services with retries, resiliency patterns, and secure authentication/authorization (RBAC).
- Optimizing model performance through prompt tuning, token management, latency reduction, and cost-efficiency strategies.
- Designing high-level and low-level system architecture diagrams (HLD/LLD) to communicate workflows, integrations, and infrastructure.
- Implementing data validation, schema enforcement, and dynamic key-value extraction for structured and unstructured datasets.
- Developing and productionizing ML models using scikit-learn, statsmodels, and traditional ML algorithms for batch and real-time inference.
- Designing observability, monitoring, and structured logging pipelines using centralized metrics and logging tools.
