- 💼 8+ years of experience in AI/ML, Data Engineering, and Cloud Systems
- 🤖 Specialized in Generative AI, LLMs, RAG, and Agentic AI systems
- ⚙️ Strong background in MLOps / LLMOps and production-grade AI deployment
- ☁️ Hands-on across AWS, Azure, and GCP ecosystems
- 📊 Experienced in building real-time pipelines, data lakehouses, and scalable AI platforms
- LLMs: GPT-4, LLaMA, Mistral, Gemini
- RAG (Retrieval-Augmented Generation), Prompt Engineering
- RLHF, Fine-tuning (LoRA, QLoRA)
- NLP, Recommendation Systems, Time Series Forecasting
- Model Explainability: SHAP, LIME, Fairlearn
- TensorFlow, PyTorch, Keras
- CNN, RNN, LSTM, Transformers
- Computer Vision (YOLO, OCR, Image Segmentation)
- MLflow, Kubeflow, Airflow
- CI/CD for ML pipelines
- Model Monitoring, Versioning, Drift Detection
- Docker, Kubernetes (EKS), Triton, TorchServe
- LLM Evaluation (TruLens, PromptLayer)
- Apache Spark, Kafka, Hadoop, Hive
- Delta Lake, Databricks, Dask
- Real-time streaming pipelines
- Data Lakehouse Architecture
S3, Glue, EMR, Lambda, SageMaker, Bedrock, Kinesis, Redshift, Athena, CloudWatch
Azure Data Factory, Azure ML, Databricks, Synapse
BigQuery, Dataflow, Pub/Sub, Vertex AI
- PostgreSQL, MySQL, MongoDB, Snowflake
- Vector DBs: FAISS, Pinecone, Weaviate
- Graph DBs: Neo4j
- Python, PySpark, SQL, Java, JavaScript
- FastAPI, Flask, REST APIs
- GDPR, HIPAA, NIST AI RMF, ISO/IEC 42001
- Model fairness, explainability, and compliance
- End-to-end AI/ML pipelines (training → deployment → monitoring)
- LLM-powered applications (RAG, agents, copilots)
- Real-time data platforms and streaming systems
- Enterprise-grade scalable cloud architectures
- LLM-based applications & Agentic AI
- Scalable MLOps / LLMOps systems
- Real-time AI pipelines with streaming data
I focus on taking AI from experimentation → production → business impact 🚀