Professional Overview
R&D Machine Learning Engineer experienced in building custom LLM solutions from data to deployment. Demonstrated expertise in advanced fine-tuning, large-scale data curation, and architecting production-ready MLOps workflows to translate system requirements into high-performance models.
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Researched reasoning transformers and LoRA/PEFT fine-tuning for SDR-focused AI.
- Fine-tuned Mistral 24B models (LoRA/QLoRA) for B2B email & LinkedIn outreach
- Ran optimized kernels distributed training on 8x A100s AWS SageMaker.
- Deployed vLLM/SGLang inference endpoints with GPT/AWQ Quantization.
- Built async data pipeline for historical & synthetic training samples.
Key Achievements / KPIs
- Outperformed Sonnet 3.7 on cold outreach personalization at 2.4x lower cost.
- 3x faster training speed; saved 10GB VRAM/GPU and reduced monthly compute costs.
- Generated 650K+ CoT-enriched training samples at $0 cost .
- Achieved 9+/10 data quality score with custom sales benchmarks.
Education
Field of Study: Computer Science Artifical Intelligence Focus
Areas of Expertise
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
NLP & Prompt Engineering
Build natural language processing systems, fine-tune large language models, and craft reliable production prompts for AI-powered products.
MLOps & AI Infrastructure
Architect scalable ML infrastructure — CI/CD for ML, model versioning, experiment tracking, monitoring, and cloud deployment on AWS, GCP, or Azure.
Data Science & Analytics
Extract business insights from raw data using statistical modelling, predictive analytics, A/B testing, and interactive dashboards.
