Sidraishfaq

AI engineer

SymLiv • Período integral

Nov 2020 - Mar 2026 • 5 yrs 4 mos

Machine Learning Engineer | Novteck (2020 – 2026) Professional Summary As a Senior ML Engineer at Novteck, I led the end-to-end development and deployment of production-grade AI systems, transitioning the company from traditional heuristics to agentic, data-driven architectures. My role bridged the gap between experimental research and scalable software engineering, ensuring that complex models delivered measurable business value. Core Responsibilities & Impact Generative AI & LLMs: Architected and optimized Retrieval-Augmented Generation (RAG) pipelines and fine-tuned Large Language Models (LLMs) using PEFT and LoRA techniques to reduce hallucination rates by 40% in enterprise chatbots. MLOps & Orchestration: Built and maintained robust CI/CD pipelines for ML using GitHub Actions, Docker, and Kubernetes. Leveraged MLflow and Weights & Biases for experiment tracking and model versioning, ensuring 99.9% uptime for production inference services. Predictive Modeling: Developed and deployed gradient-boosting models (XGBoost, LightGBM) and Deep Learning architectures (PyTorch, TensorFlow) for high-dimensional tabular data, improving prediction accuracy for client-facing analytics by 25%. Data Engineering: Engineered scalable data pipelines using Apache Spark and SQL, managing terabyte-scale datasets and implementing Feature Stores (Feast) to ensure consistency across training and serving environments. Model Optimization: Applied quantization and pruning techniques to deploy high-performance models on edge devices and cloud environments, reducing latency by 60% without significant accuracy loss. Tech Stack & Tools Languages: Python (Production-grade, OOP), SQL, Bash. Frameworks: PyTorch, JAX, Hugging Face Transformers, LangChain, Scikit-learn. Infrastructure: AWS (SageMaker), Google Cloud (Vertex AI), BentoML. Monitoring: Evidently AI, Prometheus, Grafana for drift detection and performance logging.