Senior ML Engineer LLM Systems Specialist

CH RAYAN

Senior Machine Learning Engineer LLM Systems Specialist AI Automation

**.*********@*****.*** +1-778-***-**** Vancouver, BC, Canada linkedin.com/in/chrayan

PROFILE

Senior Machine Learning Engineer with over 8 years of experience building and deploying scalable machine learning and LLM systems in production. Strong expertise in Python, PyTorch, TensorFlow, MLOps, and cloud architecture across AWS and Azure. Experienced in prompt engineering, fine-tuning, retrieval-augmented generation, and Al Automation with a proven ability to deliver high-impact solutions through cross-functional collaboration

SKILLS

Artificial Intelligence & Machine Learning

Machine Learning, Deep Learning, Reinforcement

Learning, NLP, Computer Vision, Transformer

Models, LLMs, RAG, Embeddings, Ranking Pipelines,

Fine-tuning, Prompt Engineering, Generative AI,

Few-shot & Zero-shot Learning, RLHF, Multi-modal

Models, Knowledge Graphs

Backend & Data

Backend Architecture, Distributed Systems, REST

APIs, GraphQL, FastAPI, Data Engineering, ETL

Pipelines, Apache Spark, Kafka, Airflow, Stream

Processing, Feature Engineering

ML Systems & Infrastructure

Distributed Training, Model Serving, Scalable

Inference, ML Lifecycle Management, Model

Monitoring, MLOps, Feature Stores, A/B Testing,

Model Optimization, Quantization, ONNX,

TorchServe, Triton Inference Server, Experiment

Tracking (MLflow, W&B), CI/CD for ML, Docker,

Kubernetes

Programming Languages & Frameworks

Python, Java, PyTorch, TensorFlow, Scikit-learn,

SQL, FastAPI, Pandas, NumPy, HuggingFace, Keras,

JAX, PySpark

PROFESSIONAL EXPERIENCE

Senior Machine Learning Engineer, Devlithic Technologies

•Architect and deploy scalable machine learning systems in Python using PyTorch and TensorFlow, supporting large and unstructured datasets in production environments.

•Design and run controlled experiments, define evaluation metrics such as F1 score and ROC-AUC and validate models for performance, reliability and generalization.

2022 – Present

•Build and optimize large language model applications including prompt engineering, fine-tuning, retrieval-augmented generation and context-aware response pipelines.

•Develop agentic systems with tool calling and multi-step reasoning workflows integrated with external APIs and internal knowledge bases.

•Implement deployment frameworks including Triton and TorchServer to ensure low-latency inference and production-grade model serving.

•Establish MLOps practices including experiment tracking, model versioning, automated testing and monitoring across cloud environments such as AWS and Azure.

•Partner cross-functionally with engineers, researchers, product managers, designers and business stakeholders to align AI solutions with product and growth objectives.

•Communicate complex technical concepts to technical and non-technical audiences to support adoption and decision-making. Machine Learning Engineer, Hubba

•Develop NLP and conversational AI models in Python using deep learning frameworks to power enterprise-grade virtual assistants.

•Implement large language model workflows including prompt tuning, embedding models, vector databases and context retrieval strategies. 2019 – 2021

•Engineer scalable data pipelines using SQL and cloud-native services to support feature engineering and model training.

•Optimize trade-offs between model quality, cost, inference speed, and system complexity in production ML systems.

•Collaborate with product and customer-facing teams to translate business requirements into machine learning solutions that drive product adoption and growth.

•Contribute to software development best practices including code reviews, documentation, testing and Agile delivery processes. Associate Machine Learning Engineer, Lendified

•Build predictive machine learning models for risk modeling and customer segmentation using Python, R, and SQL on structured financial datasets.

•Design experimentation frameworks, select appropriate evaluation metrics and validate model performance prior to production deployment. 2016 – 2019

•Support cloud-based ML deployment and model lifecycle management using Azure-based infrastructure and automated pipelines.

•Develop data engineering workflows for feature extraction, transformation and integration with downstream ML systems.

•Present analytical insights and model outcomes to cross-functional stakeholders, enabling data-driven product and strategy decisions. EDUCATION

Bachelor of Science

Goodwin College of Technology and Management

Contact this candidate