r/MLjobs•9 mo. ago

[FOR HIRE] Senior Applied ML Scientist

SignificantBullfrog5 · 2026-01-07T07:18:41.000Z

# Senior Applied Scientist / ML Leader ## Summary - Accomplished ML Scientist with **9+ years of experience** delivering **39 end-to-end AI projects** across multiple global enterprises. - Proven leader with deep expertise in **Generative AI, Large Language Models, Agentic AI, Transformers, NLP, Computer Vision, Recommendation Systems, and Deep Learning**. - Experienced in **building, mentoring, and scaling engineering and research teams**. - Combines strong technical execution with business acumen through an ongoing **STEM MBA**, enabling AI-driven product and strategy scaling. - **Specializations:** AI Agents, Generative AI, Multimodal LLMs, NLP, Reinforcement Learning, Recommendation Systems. --- ## Education ### Top Engineering Institute, India **Master of Engineering (ME) & Bachelor of Engineering (BE) – Computer Science** - Department Rank: 1 - CGPA: 9.3 / 10 --- ## Professional Experience ### Global Technology Company A **Principal Applied Scientist** *Feb 2021 – Aug 2025* - Accelerated execution of multi-hop workflows in cloud-based LLM platforms by **3×** using agentic frameworks. - Architected an **AI-powered customer automation ecosystem** serving **70M+ users**, integrating intent classification, chatbot agents, code generation, and function calling. - Built a **Retrieval-Augmented Generation (RAG)** system for technical documentation with **87% BERTScore** and **0.91 MRR**. - Led **15+ scientists and ML engineers** across multiple organizations to develop an LLM-powered **zero-shot personalization engine**, increasing **dwell time by 53%** and **CTR by 44%**. - Delivered **9+ document understanding systems** supporting **40+ international clients**. - Designed a **lightweight multimodal model** for product mapping and ranking, achieving: - 98% precision / 99% recall (unmatched cases) - 9× faster inference and 14.5× lower memory usage than baseline CNNs - Optimized distributed inference pipelines to process **300K+ batch inferences daily**. - Improved digital catalog quality and pricing intelligence, increasing **purchase conversion by 15%**. - Developed layout-agnostic promotion detection models achieving **99.6% recall** and **98.7% precision**. - Reduced onboarding time for new product categories from **6 months to 3 weeks** using transformer-based few-shot learning. - Built real-time tag generation and recommendation systems achieving strong ranking metrics (NDCG, mAP). - Tech lead for production ML deployment, CI/CD pipelines, and scalable inference systems. - Mentored **45+ engineers** across teams and defined long-term ML vision and roadmap. - Selected speaker at **12+ internal AI conferences** and global technical forums. --- ### Global Enterprise Software Company B **Senior Applied Scientist** *Jan 2019 – Jan 2021* - Defined the vision and roadmap for **Multimodal Large Language Models**, accelerating delivery by **25%**. - Led a **16-member team** delivering document intelligence solutions for **40+ enterprise clients**. - Designed **organization-wide Knowledge Graphs** to automate operational workloads and reduce manual support effort across **50+ engineers**. - Delivered NLP systems across multiple healthcare domains, improving customer retention by **18%**. --- ### Global E-commerce R&D Organization C **Research Scientist** *Aug 2018 – Jan 2019* - Built object detection systems from video streams, driving **2.4× revenue growth** and reducing operational headcount by **90%**. - Developed computer vision pipelines for structural analysis using OpenCV and clustering techniques. - Achieved strong deployment metrics with high confidence and IoU scores in production. --- ### Global Networking Technology Company D **Software Development Engineer** *Jan 2017 – Jul 2018* - Developed ensemble ML models to predict network performance, improving service availability by **78%**. - Built real-time anomaly detection systems with **98%+ accuracy**. - Implemented deep reinforcement learning for fault-tolerant routing. - Created real-time hand gesture recognition systems for accessibility with **99.97% inference accuracy**. --- ### Global Research Lab E **Research Fellow** *Jan 2016 – Dec 2016* - Implemented signal processing and ML models for EEG-based stimulus detection. - Integrated ML pipelines with humanoid robotics platforms for real-time decision execution. --- ## Technical Skills ### Programming Languages Python, C++, Java, Scala, C, R, JavaScript, TypeScript, React, Ruby, Go, Kotlin, Perl ### Data & Distributed Systems SQL, NoSQL (MongoDB, Cassandra, HBase, DynamoDB, Redis), Hadoop, Spark, Flink, Kafka, Storm, BigQuery ### Machine Learning & Deep Learning - Classical ML: Random Forest, XGBoost, SVM, Clustering, Ranking, A/B Testing, Bandits - Deep Learning: CNN, RNN, LSTM, BiLSTM, GNN, GANs, Diffusion Models, Transformers - Recommendation: NeuMF, BERT4Rec, Wide & Deep, DLRM, Graph-based recommenders ### NLP & LLMs BERT, RoBERTa, BART,

Senior Applied Scientist / ML Leader

Summary

Accomplished ML Scientist with 9+ years of experience delivering 39 end-to-end AI projects across multiple global enterprises.
Proven leader with deep expertise in Generative AI, Large Language Models, Agentic AI, Transformers, NLP, Computer Vision, Recommendation Systems, and Deep Learning.
Experienced in building, mentoring, and scaling engineering and research teams.
Combines strong technical execution with business acumen through an ongoing STEM MBA, enabling AI-driven product and strategy scaling.
Specializations: AI Agents, Generative AI, Multimodal LLMs, NLP, Reinforcement Learning, Recommendation Systems.

Education

Top Engineering Institute, India

Master of Engineering (ME) & Bachelor of Engineering (BE) – Computer Science

Department Rank: 1
CGPA: 9.3 / 10

Professional Experience

Global Technology Company A

Principal Applied Scientist
Feb 2021 – Aug 2025

Accelerated execution of multi-hop workflows in cloud-based LLM platforms by 3× using agentic frameworks.
Architected an AI-powered customer automation ecosystem serving 70M+ users, integrating intent classification, chatbot agents, code generation, and function calling.
Built a Retrieval-Augmented Generation (RAG) system for technical documentation with 87% BERTScore and 0.91 MRR.
Led 15+ scientists and ML engineers across multiple organizations to develop an LLM-powered zero-shot personalization engine, increasing dwell time by 53% and CTR by 44%.
Delivered 9+ document understanding systems supporting 40+ international clients.
Designed a lightweight multimodal model for product mapping and ranking, achieving:
- 98% precision / 99% recall (unmatched cases)
- 9× faster inference and 14.5× lower memory usage than baseline CNNs
Optimized distributed inference pipelines to process 300K+ batch inferences daily.
Improved digital catalog quality and pricing intelligence, increasing purchase conversion by 15%.
Developed layout-agnostic promotion detection models achieving 99.6% recall and 98.7% precision.
Reduced onboarding time for new product categories from 6 months to 3 weeks using transformer-based few-shot learning.
Built real-time tag generation and recommendation systems achieving strong ranking metrics (NDCG, mAP).
Tech lead for production ML deployment, CI/CD pipelines, and scalable inference systems.
Mentored 45+ engineers across teams and defined long-term ML vision and roadmap.
Selected speaker at 12+ internal AI conferences and global technical forums.

Global Enterprise Software Company B

Senior Applied Scientist
Jan 2019 – Jan 2021

Defined the vision and roadmap for Multimodal Large Language Models, accelerating delivery by 25%.
Led a 16-member team delivering document intelligence solutions for 40+ enterprise clients.
Designed organization-wide Knowledge Graphs to automate operational workloads and reduce manual support effort across 50+ engineers.
Delivered NLP systems across multiple healthcare domains, improving customer retention by 18%.

Global E-commerce R&D Organization C

Research Scientist
Aug 2018 – Jan 2019

Built object detection systems from video streams, driving 2.4× revenue growth and reducing operational headcount by 90%.
Developed computer vision pipelines for structural analysis using OpenCV and clustering techniques.
Achieved strong deployment metrics with high confidence and IoU scores in production.

Global Networking Technology Company D

Software Development Engineer
Jan 2017 – Jul 2018

Developed ensemble ML models to predict network performance, improving service availability by 78%.
Built real-time anomaly detection systems with 98%+ accuracy.
Implemented deep reinforcement learning for fault-tolerant routing.
Created real-time hand gesture recognition systems for accessibility with 99.97% inference accuracy.

Global Research Lab E

Research Fellow
Jan 2016 – Dec 2016

Implemented signal processing and ML models for EEG-based stimulus detection.
Integrated ML pipelines with humanoid robotics platforms for real-time decision execution.

Technical Skills

Programming Languages

Python, C++, Java, Scala, C, R, JavaScript, TypeScript, React, Ruby, Go, Kotlin, Perl

Data & Distributed Systems

SQL, NoSQL (MongoDB, Cassandra, HBase, DynamoDB, Redis), Hadoop, Spark, Flink, Kafka, Storm, BigQuery

Machine Learning & Deep Learning

Classical ML: Random Forest, XGBoost, SVM, Clustering, Ranking, A/B Testing, Bandits
Deep Learning: CNN, RNN, LSTM, BiLSTM, GNN, GANs, Diffusion Models, Transformers
Recommendation: NeuMF, BERT4Rec, Wide & Deep, DLRM, Graph-based recommenders

NLP & LLMs

BERT, RoBERTa, BART, T5, GPT-family, Claude, Gemini, LLaMA, Mistral, Mixtral, Qwen, Falcon, Vicuna

Generative AI & Agentic Systems

RAG, Fine-tuning (SFT, PEFT, LoRA, QLoRA), RLHF, DPO, Prompt Engineering, Multi-Agent Systems

MLOps & Infra

MLflow, Airflow, Ray, Kubeflow, Triton, TorchServe, BentoML, vLLM, Docker, Kubernetes, Terraform

Computer Vision

YOLO, SSD, Faster R-CNN, Mask R-CNN, UNet, EfficientDet, Vision Transformers

Awards & Achievements

Employee of the Quarter (4×) across multiple global business units (2024).
National-level innovation awards for AI product prototypes (2022).
Multiple organizational excellence and innovation awards (2021–2023).
National and state academic ranking awards (Top 1–4 among ~1M students).
Recipient of national STEM scholarships and top 1% rankings in science olympiads.

Posted Jan 7, 2026, 7:18 AM UTC