AI/ML engineer Resume

Candidate Pitch:

• Experienced AI/ML Engineer with ~10 years of end-to-end expertise across GenAI, RAG architectures, LLM microservices, NLP pipelines, predictive analytics, and full-stack Python development, delivering enterprise AI solutions for BFSI, Retail, Healthcare, and Manufacturing. • Skilled in building production-grade GenAI systems with LangChain, LangGraph, CrewAI, ToolExecutor, FAISS, vector stores, Whisper STT, prompt engineering, guardrails, LLM evaluation, ONNX optimization, LoRA/QLoRA fine-tuning, RLHF, and explainability frameworks like SHAP/LIME. • Strong expertise in NLP & Document Intelligence, including spaCy, text classification, NER pipelines, OCR (Textract, Form Recognizer), ICD-10/SNOMED mapping, rule-based validation, and large-scale data-quality enhancement for regulated datasets. Hands-on in ML & deep learning, covering time-series forecasting (LSTM, Prophet), classification/segmentation models, anomaly detection, feature engineering, OpenCV defect detection, XGBoost/LightGBM (fraud/AML), and end-to-end deployment using TensorFlow, PyTorch, and Keras. • Experienced in developing backend & microservices using FastAPI, Django, Flask, REST APIs, RBAC, authentication, audit logging, workflow automation, and full-stack support with React, Streamlit, Dash, Plotly, GraphQL, and UI components for analysts and operational teams. • Strong background in data engineering, including PySpark pipelines, Kafka streaming, Azure Data Factory, ETL workflows, schema validations, feature stores, data-drift monitoring, and real-time LLM inference using Kafka, Kinesis, and Pub/Sub. • Proficient in cloud & MLOps across AWS, GCP, and Azure with Docker, Kubernetes, MLflow, Model Registry, VPC-isolated deployments, IAM policies, private endpoints, Terraform/CDK basics, GPU tuning, blue-green deployments, lineage tracking, and observability via OpenTelemetry, Grafana, Kibana. • Experienced in designing secure & compliant AI systems aligned with HIPAA, GDPR, FINRA/SEC/SOX, SOC2, AML/KYC, PHI/PII masking, audit trails, governance documentation, and model risk management practices. • Hands-on with data tools & analytics platforms including SQL, Pandas, Neo4j (Cypher queries), and visualization frameworks supporting merchandising, clinical, financial, and manufacturing workflows. • Strong experience collaborating with cross-functional teams—compliance SMEs, clinicians, merchandisers, mechanical engineers, QA, and cloud architects—to validate outputs and deliver business-ready AI capabilities. • Operated in Agile/Scrum environments, driving sprint planning, backlog grooming, UAT cycles, and production rollouts across multiple domains. PTU