Hire Chaitanya A. - Fine-Tuning Engineer

* Architect and deploy end-to-end ML pipelines on AWS SageMaker and Lambda — covering data ingestion, feature engineering, training, and serverless model serving. * Implement MLflow for experiment tracking and model registry * engineer LLM fine-tuning workflows (LoRA/PEFT) with Hugging Face and RAG pipelines using LangChain + Pinecone. * Build ML feature pipelines on AWS (S3, Glue, Redshift) with PySpark and SQL * integrate Prometheus + Grafana dashboards, reducing MTTR by 42%. * Deliver multi-cloud AI integrations across AWS Bedrock, Azure OpenAI, and GCP Vertex AI * conduct A/B experiments and statistical hypothesis testing for model evaluation. * Define SLOs, error budgets, and on-call runbooks * enforce Terraform IaC and GitHub Actions CI/CD for repeatable AI infrastructure.

* Designed ML feature engineering and embedding pipelines feeding production models; applied statistical modeling and hypothesis testing for model validation. * Integrated Prometheus and Grafana for ML pipeline observability — reduced on-call escalations by 35% and achieved 40% faster MTTR. * Applied IAM, RBAC, KMS encryption, and VPC segmentation across AWS and Azure ML workloads; deployed Terraform IaC and GitHub Actions CI/CD. * Built ETL workflows on AWS (S3, Lambda, DynamoDB, Athena) and GCP (BigQuery, Cloud Storage) for enterprise-scale data integration. * Designed Azure Data Factory pipelines into Synapse Analytics; improved pipeline throughput by 50% through optimized partitioning and query patterns.