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Data Scientist and AI Engineer skilled in machine learning, NLP, data analytics, Gen AI seeking to apply technical expertise in AI-driven problem solving and automation to deliver impactful business outcomes within a forward-thinking organization.


  Results-driven AI/ML Developer and Python Developer with over 10 years of experience in designing, developing, and deploying enterprise-grade AI/ML, data engineering, and cloud-based applications across manufacturing, finance, healthcare, and retail domains.
  Applied prompt engineering, dynamic chaining, and agent-based architectures using LangChain to enhance response accuracy and explainability.
  Proficient in integrating LLMs (OpenAI, Hugging Face) into production using FastAPI and Flask, with hands-on experience in MLOps, CI/CD, and model monitoring.
  Experienced AI/ML Developer skilled in building, training, and deploying machine learning and deep learning models using Python, PyTorch, TensorFlow, and AWS.
  Experienced in designing and deploying NLP pipelines for sentiment analysis, document summarization, named entity recognition (NER), and translation using Hugging Face Transformers, spaCy, and GPT-based architectures.
  Hands-on experience in computer vision, building and deploying object detection, OCR, and image segmentation solutions using OpenCV, TensorFlow, and Keras for real-time visual inference.
  Highly skilled in Python development for data manipulation, transformation, and analytics using Pandas, NumPy, Scikit-learn, PySpark, and Matplotlib, with proven success in building scalable ETL pipelines on Azure Databricks and AWS Glue to process multi-terabyte datasets via batch and streaming ingestion.
  Experienced in developing and deploying GenAI and machine learning solutions, including LLMs, Retrieval-Augmented Generation (RAG), LangChain orchestration, and Hugging Face Transformer fine-tuning, to deliver predictive intelligence and automate business workflows.
  Strong expertise in Delta Lake architecture (bronze–silver–gold) for ACID transactions, schema evolution, and query optimization, as well as in metadata-driven, incremental data pipelines integrating structured and unstructured data from diverse sources into Azure Data Lake Gen2, Synapse Analytics, Redshift, and HDFS.
  Demonstrated ability to optimize Spark performance through partitioning, caching, and adaptive query execution, achieving up to 60% improvement in compute efficiency, and faster SLA adherence.
  Proficient in implementing CI/CD pipelines using Azure DevOps, Jenkins, Terraform, Docker, and Kubernetes, and in establishing data validation frameworks with PySpark and Great Expectations to ensure data accuracy and integrity.
  Extensive experience leveraging Azure (ADF, Synapse, Event Hubs, Purview, Delta Live Tables), and AWS (S3, EMR, Redshift, Kinesis, Lambda, CloudFormation, CloudWatch) for secure, compliant, and cost-optimized data ecosystems.
  Skilled in using AI-assisted development tools, such as GitHub Copilot, for intelligent code generation, testing, and documentation, enhancing development efficiency and quality assurance.
  Experienced in performing advanced analytics and visualization with Power BI, Tableau, and Excel (DAX, SQL) to deliver actionable business insights and KPI dashboards for stakeholders. Recognized for strong collaboration with cross-functional ML, DevOps, and product teams in Agile/Scrum environments, leading cloud migration, data governance, and compliance initiatives (GDPR, PII masking) to build reliable, scalable, and insight-driven enterprise data solutions.


Results-driven AI/ML Engineer with 3 years of experience in architecting, developing, and deploying scalable NLP, Generative AI, and machine learning solutions on Azure. Demonstrated expertise in the end-to-end ML lifecycle, including model development using PyTorch, Transformers, LLMs via Azure OpenAI, and fine-tuning. Proficient in containerized deployment with Docker and asynchronous API integration using FastAPI, Celery, and Redis. Skilled in leveraging RAG systems, Chroma vector databases, and MLOps principles to optimize application performance and efficiency. A dedicated collaborator passionate about transforming complex data into robust, production-ready AI applications.

Passionate AI/ML Engineer with 10 years of IT experience, including 7+ years in AIML Data Engineering and ETL Development, specializing in Data Integration and 2 years in Python development. Skilled in designing and deploying scalable data pipelines and infrastructure, with a solid foundation in data modeling, ETL processes, and big data technologies. Proficient in building scalable AI pipelines leveraging Retrieval-Augmented Generation (RAG), Transformer-based LLMs (e.g., GPT-4, LLaMA, Claude), and advanced NLP techniques for real-time document intelligence, anomaly detection, and event extraction. Familiar with implementing LangChain and LangGraph for agentic workflows and integrating multi-hop, hybrid, and semantic search in enterprise GenAI solutions Integrated Databricks with cloud services like AWS and Azure for seamless data processing and storage. Experience in Data Architecture, Design Data Ingestion, Design Data Pipelines, Design the Domain Model Experience in SPARK - Spark Streaming, Spark SQL, Spark Data Frames, Spark Performance Tuning Hands-on Experience in PYSPARK, PYTHON, SQL. Developed and maintained data pipelines using PySpark and Databricks, ensuring efficient processing of large datasets. Designed ETL workflows on Databricks to ingest, clean, and transform data for analytics and reporting purposes. Optimized PySpark code to improve performance and scalability in distributed computing environments. Collaborated with data scientists to deploy real-time ML models on Databricks, utilizing PySpark for streaming data processing and prediction. Good Understanding of CICD Tools Jenkins, Git, Bid Bucket for deployment of code to various Environments Experience in agile SCRUM methodology, sprint planning, creating backlogs/stories, sprint reviews, delivery Demonstrated ability to optimize PySpark code for performance, leading to significant improvements in execution speed and resource utilization. Hands-on experience with version control systems like Git and collaborative development practices using GitHub/Bitbucket Good Knowledge of Machine Learning, Deep Learning, LLMs, RAG, natural language processing (NLP) techniques for text analysis, sentiment analysis, and chatbot development in Artificial Intelligence AI. Analyze data to identify patterns, trends, and insights that can drive decision-making and improve AI models. Collaborated closely with data engineering, analytics, and operations teams to deliver scalable, efficient, and high-performance data pipelines

Jitendra Agrawal

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AI/ML Engineer

Software Engineer

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Bachelor of Science - Computer Science

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AI/ML Engineer

Software Engineer

Bachelor of Science - Computer Science

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