Raghu S.

I'm a machine learning engineer with 7+ years shipping forecasting, computer-vision, NLP and GenAI systems from experiment to production. Most recently I was a core contributor to the forecasting engine behind an AI customer-opportunity platform for enterprise retail brands, building PySpark feature pipelines and TensorFlow training and inference workflows on BigQuery and Dataproc. $20K+ earned across 22 Upwork jobs | 100% Job Success | Google Cloud and AWS How I help teams: • ML and forecasting models: classification, churn and winback prediction, forecasting, NLP and computer vision, from feature engineering to model serving and monitoring • Production deployment on GCP and AWS using Cloud Run, GKE, Vertex AI, Dataflow and Amazon Bedrock, with Docker and CI/CD • ETL/ELT and ML data pipelines using Python, SQL, BigQuery, PySpark, Airflow, Snowflake, Databricks and cloud-managed services • Python and FastAPI backends, REST APIs, Docker, CI/CD and Kubernetes • GenAI: production RAG systems with ingestion, hybrid retrieval, reranking, citations, grounding checks, evaluation and observability • LangGraph and agentic workflows with persistent state, structured outputs, tool calling, human approval, retries and controlled failure handling • LLM and RAG evaluation harnesses with labeled test sets, retrieval metrics (recall@k, MRR), regression gates and failure analysis • Secure MCP servers and API integrations with authentication, least-privilege access, validation and audit logging • Operational dashboards and client-facing AI interfaces that expose quality, latency, cost and failure signals My public portfolio contains tested implementations for RAG evaluation, LangGraph orchestration, secure MCP access, AWS Bedrock deployment and AI operations monitoring. My commercial experience includes retail forecasting, hierarchical ML systems, PySpark feature pipelines, TensorFlow training and inference, semantic search, Cloud SQL, Dataproc and multi-environment delivery. I start by understanding your business outcome, current architecture, data constraints, failure cases and definition of done. You receive transparent milestones, tested code, deployment guidance, observability and maintainable handover documentation. Send me your problem, current stack and expected outcome. I will recommend the smallest practical first milestone. Optional: I also run a free 10-minute test of AI assistants (send a chatbot link and 3 things it must never get wrong, and I will send a one-page result within 24 hours).