Syed Moiz Ali

Role: Senior ML Consultant  |  Stack: Python, RAG pipeline, Hugging Face, vector stores, FastAPI, AWS SageMaker

Designed and deployed a production-grade Retrieval-Augmented Generation (RAG) system for domain-specific conversational AI, replacing a brittle rule-based NLP system. The architecture combined a dense retrieval layer with a fine-tuned generative model, applying advanced prompt engineering and context-window management techniques to maintain answer relevance at scale.

• Achieved an 18% uplift in internal NLP accuracy benchmarks relative to the previous system.
• Implemented retrieval evaluation pipelines (precision@k, MRR, NDCG) to drive iterative RAG improvements.
• Deployed serving infrastructure on AWS SageMaker with autoscaling inference endpoints, maintaining low-latency response under variable load.

Role: Senior ML Consultant  |  Stack: Python, XGBoost, LightGBM, PyTorch (LSTM), Optuna, AWS SageMaker, MLflow

Designed and operationalised complex ensemble ML architectures for industrial time-series demand forecasting. The solution combined the pattern-capture strengths of gradient boosting trees (XGBoost, LightGBM) with the sequential memory of LSTM networks, with Optuna-driven hyperparameter search across both model families.

• Delivered double-digit percentage accuracy improvements over all incumbent baseline forecasting models.
• Implemented robust feature engineering pipelines for lag features, rolling statistics, and Fourier seasonality decomposition.
• Tracked all experiments using MLflow, enabling reproducible comparison and model governance aligned with enterprise standards.

Role: Senior ML Consultant  |  Stack: Terraform, AWS (ECS, SageMaker, RDS, S3, VPC), Docker, GitHub Actions, CloudWatch

Led a full MLOps infrastructure migration from manually managed legacy environments to a fully automated, reproducible cloud platform. Authored all Terraform modules for network, compute, storage, and monitoring resources, and designed CI/CD pipelines integrating model training, evaluation gates, and blue-green deployment to AWS ECS and SageMaker endpoints.

• Reduced deployment cycle time by 60% — from multi-day manual processes to sub-hour automated releases.
• Cut pipeline failure rate by 85% through automated pre-deployment validation, integration testing, and infrastructure drift detection.
• Established infrastructure-as-code standards and runbooks adopted across all future platform initiatives.

Role: Senior ML Consultant  |  Stack: Python, TensorFlow/Keras CNN, Optuna, SHAP, LIME, FastAPI, Docker

Engineered a scalable document classification system using convolutional neural networks (CNNs) with systematic hyperparameter optimisation via Optuna and post-hoc explainability via SHAP and LIME. The system was designed to meet both accuracy and interpretability requirements demanded by enterprise compliance teams.

• Achieved a 94% precision rate on multi-class document classification in production.
• SHAP value explanations provided per-prediction feature attributions, satisfying audit and regulatory transparency requirements.
• Deployed as a containerised FastAPI microservice with asynchronous batch processing support and structured logging for observability.

Role: Senior ML Consultant  |  Stack: Python, FastAPI, Docker, Kubernetes, PostgreSQL, AWS ECS, CloudWatch

Designed and operationalised a horizontally scalable ML inference platform built on FastAPI microservices orchestrated across Docker and Kubernetes clusters. The platform served multiple model endpoints under variable traffic loads with autoscaling, health check automation, and zero-downtime rolling deployments.

• Maintained 99.2% API availability during sustained 300% traffic spike events — validating the platform's elastic architecture design.
• Implemented circuit breakers, request queuing, and graceful degradation patterns to protect downstream services under extreme load.
• Instrumented all services with structured metrics, enabling real-time SLA monitoring and proactive capacity planning.