Bikramjeet Singh
Delhi, India
Bikramjeet Singh
Data Scientist, AI/Machine Learning Engineer
Category : Artificial intelligence (AI)
A Data Scientist focused on building intelligent systems that turn data into meaningful impact. My experience spans data analysis, machine learning, and deep learning, with hands-on work in LangChain and RAG to create advanced AI applications. Proficient in Python, SQL, and Excel, I approach projects end to end—from extracting insights to developing models that scale. Looking ahead, my goal is to grow into a full-stack AI professional working at the intersection of data, automation, and product innovation. I dont't just build models, I build pipelines that scale.
Working hours
- Monday:08h00 To 18h00
- Tuesday:08h00 To 18h00
- Wednesday:08h00 To 18h00
- Thursday:08h00 To 18h00
- Friday:08h00 To 18h00
- Saturday:Not available
- Sunday:Not available
![Multi-modal Computer Vision & OCR Solutions]()
I offer advanced computer vision services to help machines "see" and interpret visual data. This includes building multi-modal diagnostic systems that combine image recognition with textual analysis ...![Industrial Predictive Analytics & Forecasting]()
Leveraging my experience in high-stakes environments, I develop predictive models to forecast performance and optimize operations. I specialize in processing SCADA and IoT sensor data to build ...![Custom LLM Fine-Tuning & Specialized Translation]()
I provide expert fine-tuning for Large Language Models (Llama, Qwen 2.5, ByT5) to adapt them for specialized tasks or niche domains. I specialize in Parameter-Efficient Fine-Tuning (PEFT) using LoRA ...![Enterprise RAG Pipeline Development]()
I build advanced Retrieval-Augmented Generation (RAG) pipelines that connect your private data—from PDFs and databases to live APIs—directly to an LLM without the need for expensive retraining. My sys ...
Developed an end-to-end machine learning pipeline using Python to forecast long-term wind turbine per-
formance, achieving over 90 percent correlation between predicted and actual wind speeds.
◦ Integrated and preprocessed high-frequency SCADA data (2021–2024) with 20 years of ERA5 reanalysis
data, employing filtering and interpolation techniques to align complex datasets.
◦ Engineered 72 input features to enhance yield prediction accuracy, enabling high-fidelity production estima-
tion and improved risk assessment for energy production.
◦ Generated scaled wind speed time series and computed the Wind Energy Index (WEI) for monthly and
annual performance benchmarking.
◦ Analyzed microscale atmospheric effects to provide actionable insights into turbine performance variations
and long-term yield analysis.
formance, achieving over 90 percent correlation between predicted and actual wind speeds.
◦ Integrated and preprocessed high-frequency SCADA data (2021–2024) with 20 years of ERA5 reanalysis
data, employing filtering and interpolation techniques to align complex datasets.
◦ Engineered 72 input features to enhance yield prediction accuracy, enabling high-fidelity production estima-
tion and improved risk assessment for energy production.
◦ Generated scaled wind speed time series and computed the Wind Energy Index (WEI) for monthly and
annual performance benchmarking.
◦ Analyzed microscale atmospheric effects to provide actionable insights into turbine performance variations
and long-term yield analysis.
- 🇬🇧 English
- 🇮🇳 Hindi
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