Looks like you have JavaScript disabled. For the full Twine experience, you will need to re-enable it.

I am a machine learning engineer with 3+ years of hands-on experience in fine-tuning large language models, retrieval-augmented generation, and production-grade MLOps across AWS, GCP, and Azure. I’ve delivered tangible business impact by improving conversions, search relevance, and user engagement through transformer-based NLP, experimentation, and robust A/B testing. I thrive on turning ambiguous problems into scalable AI solutions, automating end-to-end training, deployment, and monitoring pipelines, and collaborating with cross-functional teams to ensure safe and reliable deployments. I enjoy exploring new models and cloud ML infrastructure, building scalable pipelines for real-time ranking and forecasting, and creating interpretable AI systems that stakeholders in policy, product, and legal can audit and trust.…I am a machine learning engineer with 3+ years of hands-on experience in fine-tuning large language models, retrieval-augmented generation, and production-grade MLOps across AWS, GCP, and Azure. I’ve delivered tangible business impact by improving conversions, search relevance, and user engagement through transformer-based NLP, experimentation, and robust A/B testing. I thrive on turning ambiguous problems into scalable AI solutions, automating end-to-end training, deployment, and monitoring pipelines, and collaborating with cross-functional teams to ensure safe and reliable deployments. I enjoy exploring new models and cloud ML infrastructure, building scalable pipelines for real-time ranking and forecasting, and creating interpretable AI systems that stakeholders in policy, product, and legal can audit and trust.

Jaswanth Reddy

Data Scientist, AI Engineer, AI Developer, +3





I am a machine learning engineer with 3+ years of hands-on experience in fine-tuning large language models, retrieval-augmented generation, and production-grade MLOps across AWS, GCP, and Azure. I’ve delivered tangible business impact by improving conversions, search relevance, and user engagement through transformer-based NLP, experimentation, and robust A/B testing. I thrive on turning ambiguous problems into scalable AI solutions, automating end-to-end training, deployment, and monitoring pipelines, and collaborating with cross-functional teams to ensure safe and reliable deployments. I enjoy exploring new models and cloud ML infrastructure, building scalable pipelines for real-time ranking and forecasting, and creating interpretable AI systems that stakeholders in policy, product, and legal can audit and trust.…I am a machine learning engineer with 3+ years of hands-on experience in fine-tuning large language models, retrieval-augmented generation, and production-grade MLOps across AWS, GCP, and Azure. I’ve delivered tangible business impact by improving conversions, search relevance, and user engagement through transformer-based NLP, experimentation, and robust A/B testing. I thrive on turning ambiguous problems into scalable AI solutions, automating end-to-end training, deployment, and monitoring pipelines, and collaborating with cross-functional teams to ensure safe and reliable deployments. I enjoy exploring new models and cloud ML infrastructure, building scalable pipelines for real-time ranking and forecasting, and creating interpretable AI systems that stakeholders in policy, product, and legal can audit and trust.

Available to hire

I am a machine learning engineer with 3+ years of hands-on experience in fine-tuning large language models, retrieval-augmented generation, and production-grade MLOps across AWS, GCP, and Azure. I’ve delivered tangible business impact by improving conversions, search relevance, and user engagement through transformer-based NLP, experimentation, and robust A/B testing. I thrive on turning ambiguous problems into scalable AI solutions, automating end-to-end training, deployment, and monitoring pipelines, and collaborating with cross-functional teams to ensure safe and reliable deployments.

I enjoy exploring new models and cloud ML infrastructure, building scalable pipelines for real-time ranking and forecasting, and creating interpretable AI systems that stakeholders in policy, product, and legal can audit and trust.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Language

English

Fluent

Work Experience

ML Engineer at OpenAI

January 1, 2025 - November 28, 2025

Improved enterprise customer adoption by 19% by fine-tuning GPT-based ranking and recommendation models using domain-specific signals, user embeddings, and real-time behavioural feedback loops. Enhanced search relevance by 15% by optimizing embedding models to handle long-tail prompts, multilingual queries, and noisy user text. Reduced inference latency by 32% by profiling tokenization bottlenecks, applying kernel-level optimizations, deploying quantized ONNX models on GPU-accelerated endpoints. Automated end-to-end LLM training, evaluation, and canary rollout using Airflow + MLflow, cutting deployment cycles by 45% and standardizing safe-launch workflows. Introduced RAG evaluation pipelines with hallucination checks, grounding scores, and response-consistency metrics, enabling safety teams to validate outputs before enterprise releases. Added interpretability layers (attention maps, SHAP embeddings, and prompt-level decision traces) so legal, policy, and product teams could audit mode

Associate ML Engineer at Fractal Analytics

June 1, 2023 - June 1, 2023

Achieved 92%+ accuracy across healthcare risk scoring, customer segmentation, and retail demand models by selecting algorithms and features suited to each domain. Increased forecasting precision by 23% using CNN-based shelf analytics and LSTM models tuned for promo cycles and regional behavior. Reduced API latency by 35% through containerized FastAPI/Docker deployments and optimized preprocessing pipelines. Automated data prep, drift detection, and scheduled retraining with Airflow/Prefect to avoid manual refreshes during peak periods. Designed dashboards linking predictive outputs to metrics like stockout probability and patient risk tiers to support business decisions. Collaborated with client stakeholders to align predictive outputs with KPIs in revenue forecasting, demand planning, and healthcare risk assessment. Improved model stability with domain-aware feature engineering, including ICD-code grouping and SKU-level seasonality patterns.

Associate ML Engineer at Fractal Analytics

March 1, 2021 - June 1, 2023

Achieved 92%+ accuracy across healthcare risk scoring, customer segmentation, and retail demand models by selecting algorithms and features suited to each domain. Increased forecasting precision by 23% using CNN-based shelf analytics and LSTM models tuned for promo cycles and regional behaviour. Reduced API latency by 35% through containerized FastAPI/Docker deployments and optimized preprocessing pipelines. Automated data prep, drift detection, and scheduled retraining with Airflow/Prefect to avoid manual refreshes during peak periods. Designed dashboards linking predictive outputs to metrics like stockout probability and patient risk tiers to support business decisions. Collaborated with client stakeholders to align predictive outputs with KPIs in revenue forecasting, demand planning, and healthcare risk assessment. Improved model stability with domain-aware feature engineering, including ICD-code grouping and SKU-level seasonality patterns.