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AI/ML EngineerGenerative AI · Applied ML · MLOps

Shreyas Mysore Sudesh.

4+ years building production AI solutions across financial services — Generative AI, RAG, agentic workflows, predictive analytics, and scalable ML platforms.

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Financial services · Production AI LinkedIn GitHub
4+ yrs

Experience

45%

Retrieval Time Reduced

35%

Investigation Effort Reduced

28%

Answer Accuracy Improved

50%

Data Prep Time Reduced

38%

Pipeline Runtime Reduced

01 / Selected work

Applied intelligence.

Financial research, predictive modeling, and intelligent document processing.

01 / Generative AI & RAG

Enterprise Financial Research Assistant

Financial document intelligence with context-grounded responses and multi-step financial research.

System architecture
DocumentsEmbeddingsVector SearchLangGraphLLMs
  • Built a financial document intelligence platform using Python, LangChain, LangGraph, OpenAI APIs, embeddings, and vector databases to retrieve relevant information from financial documents and generate context-grounded responses.
  • Implemented agentic workflows, MCP-based tool integration, retrieval evaluation, and LLM observability to support multi-step financial research and improve response reliability.
02 / Machine Learning

Financial Data Intelligence & Predictive Modeling

Transaction classification, customer analytics, and financial forecasting with reusable ML workflows.

System architecture
DataPySparkDelta LakeFeaturesXGBoost
  • Developed predictive models for transaction classification, customer analytics, and financial forecasting using Python, Scikit-learn, XGBoost, and feature engineering to support data-driven business decisions.
  • Engineered scalable data pipelines with PySpark, Databricks, and Delta Lake, integrating data transformation, model validation, and experiment tracking into reusable ML workflows.
03 / NLP and Computer Vision

Intelligent Document Processing & Text Analytics

Automated analysis of unstructured business documents with NLP, OCR, and computer vision.

System architecture
DocumentsOCRBERTspaCyAnalytics
  • Developed NLP pipelines for document classification and entity extraction using BERT, Hugging Face Transformers, and spaCy to automate the analysis of unstructured business documents.
  • Integrated OCR and computer vision techniques using OpenCV and YOLO with Python-based analytics workflows to streamline document and image processing and improve information extraction.
02 / System design

Inside the intelligence.

Research assistants, reusable predictive workflows, and intelligent document analysis.

Financial Research RAG
DOCUMENTSEMBEDDINGSVECTOR SEARCHLANGGRAPHLLMS
Financial Research RAG

Context-grounded financial research with semantic retrieval, agentic workflows, and LLM evaluation.

  1. 01

    Documents

    Financial documents provide the context for research and document intelligence.

  2. 02

    Embeddings

    Embeddings represent document content for semantic retrieval.

  3. 03

    Vector Search

    Vector databases retrieve relevant financial information.

  4. 04

    LangGraph

    Agentic workflows coordinate multi-step research and tool calling.

  5. 05

    LLMs

    OpenAI APIs generate context-grounded responses, supported by retrieval evaluation and observability.

03 / Technical expertise

The AI engineering toolkit.

From language models and predictive learning to deployment, evaluation, and reliable infrastructure.

Generative AI & LLM Engineering

Generative AILLMsRAGAgentic AILLM AgentsLangChainLangGraphLlamaIndexPrompt EngineeringTool/Function CallingOpenAI APIsEmbeddingsVector DatabasesSemantic SearchLLM EvaluationMCP

Machine Learning

Supervised & Unsupervised LearningFeature EngineeringModel EvaluationClassificationRegressionClusteringTime-Series ForecastingScikit-learnXGBoostLightGBM

Deep Learning & NLP

PyTorchTensorFlowKerasTransformersHugging FaceBERTGPT ModelsNLPNERText ClassificationSentence TransformersspaCy

Computer Vision

OpenCVYOLOImage ClassificationObject DetectionOCRImage SegmentationTransfer Learning

MLOps & Model Deployment

MLflowKubeflowDockerKubernetesCI/CDGitHub ActionsJenkinsAirflowModel RegistryModel MonitoringFeature Stores

Model Serving & Optimization

ONNXONNX RuntimeTriton Inference ServervLLMModel ServingInference OptimizationModel QuantizationA/B TestingHyperparameter TuningOptuna

Monitoring & Responsible AI

AI ObservabilityModel Performance MonitoringData/Model DriftModel ExplainabilityBias DetectionResponsible AILogging

Cloud & AI Platforms

AWSSageMakerBedrockLambdaECREC2S3Azure Machine LearningAzure AIGoogle CloudVertex AI

Programming & Scripting

PythonSQLBashPandasNumPySciPy

Data Engineering & Distributed Computing

Apache SparkPySparkApache KafkaDatabricksETL/ELTData ModelingDelta LakeParquetData LakesData Warehousing

Databases & Storage

PostgreSQLMySQLMongoDBRedisSnowflake

Visualization & Engineering

Power BITableauPlotlyMatplotlibGitGitHubGitLabAgileScrumSDLCCode ReviewsUnit TestingIntegration Testing
04 / Experience

Production AI, measurable impact.

Financial research, predictive analytics, and intelligent automation across Morgan Stanley, HCLTech, and Zensar Technologies.

01

AI/ML Engineer @ Morgan Stanley

New York · Jan 2026 — Present
  • Developed an enterprise RAG platform for financial research and document intelligence using Python, LangChain, LangGraph, OpenAI APIs, embeddings, and vector search, reducing analyst information retrieval time by 45%.
  • Engineered agentic AI workflows for financial document analysis and research automation using LLM tool calling and MCP, reducing manual investigation effort by 35%.
  • Implemented LLM evaluation pipelines, semantic retrieval validation, and AI observability to improve response consistency and answer accuracy by 28% across prioritized use cases.
  • Deployed scalable AI services using Docker, Kubernetes, AWS Bedrock, Lambda, Amazon ECR, and CI/CD pipelines, reducing deployment cycles from days to hours.
  • Collaborated with product, data, and business teams to translate financial research and operational requirements into production-ready RAG and agentic AI applications.
  • Established reusable engineering standards for prompt engineering, model evaluation, monitoring, and responsible AI, reducing duplicated development effort by 25% across AI initiatives.
45%
Retrieval time ↓
35%
Investigation effort ↓
28%
Answer accuracy ↑
Hours
Deployment cycles
25%
Duplicated effort ↓
02

AI/ML Engineer @ HCLTech

India · Mar 2023 — Aug 2024
  • Developed machine learning solutions for financial data analysis, transaction classification, and business forecasting using Python, Scikit-learn, XGBoost, and feature engineering, improving model performance by 22%.
  • Built scalable ETL/ELT pipelines using PySpark, Apache Spark, Databricks, and Delta Lake to process financial and customer datasets, reducing data preparation time by 50%.
  • Implemented classification, regression, and clustering models to identify customer behavior patterns, support financial insights, and improve decision-support accuracy by 20%.
  • Optimized distributed data processing and Spark transformations for large datasets, reducing pipeline execution time by 38% and accelerating downstream analytics and model refreshes.
  • Developed reusable ML pipelines for feature engineering, model training, validation, and experiment tracking, reducing development effort by 30% across analytics projects.
  • Built predictive analytics and time-series forecasting solutions using Python, Pandas, NumPy, and XGBoost to support business planning and reduce forecast error by 18%.
22%
Model performance ↑
50%
Data prep time ↓
20%
Decision accuracy ↑
38%
Pipeline runtime ↓
30%
Development effort ↓
18%
Forecast error ↓
03

Data Scientist @ Zensar Technologies

India · Apr 2021 — Feb 2023
  • Developed predictive modeling solutions using Python, Pandas, NumPy, Scikit-learn, and XGBoost to support business forecasting and classification, improving prediction performance by 18%.
  • Applied supervised and unsupervised learning techniques, including classification, regression, and clustering, to identify customer segments, behavioral trends, and operational patterns.
  • Built NLP pipelines for text classification, entity extraction, and document processing using BERT, Hugging Face Transformers, spaCy, and Sentence Transformers, reducing manual review effort by 40%.
  • Developed computer vision solutions using OpenCV, YOLO, and OCR for image classification and automated document or image analysis, reducing manual inspection time by 30%.
  • Created interactive dashboards and analytical visualizations using Power BI, Tableau, Matplotlib, and Plotly to communicate model results and business trends, reducing reporting turnaround by 25%.
  • Implemented model validation and performance monitoring workflows to compare algorithms, improve prediction reliability, and reduce production model rework by 20%.
18%
Prediction performance ↑
40%
Review effort ↓
30%
Inspection time ↓
25%
Reporting time ↓
20%
Model rework ↓
05 / Background

Research mindset. Engineering discipline.

AI/ML Engineer with 4+ years of experience across financial services, machine learning, predictive analytics, and production AI solutions, translating complex business requirements into scalable, data-driven applications.

Hands-on expertise in Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic AI, LangChain, LangGraph, LlamaIndex, prompt engineering, and tool/function calling.

Experienced in developing and evaluating machine learning models using Python, Scikit-learn, XGBoost, Pandas, NumPy, and PyTorch, including classification, regression, forecasting, feature engineering, and model validation.

Skilled in building scalable data pipelines and distributed processing workflows using Apache Spark, PySpark, Databricks, Delta Lake, and Apache Kafka to support enterprise analytics and ML workloads.

Proficient in deploying and operationalizing AI/ML applications using AWS, Amazon Bedrock, SageMaker, Docker, Kubernetes, MLflow, and CI/CD, with a focus on model serving, monitoring, scalability, and production reliability.

Strong background in NLP, computer vision, and data visualization, with experience in Transformers, BERT, Hugging Face, spaCy, OpenCV, YOLO, Power BI, and Tableau.

// 06 · resume

One PDF, full archive.

latest resume

Shreyas Mysore Sudesh — AI/ML Engineer

Full role history, AI/ML projects, education, and the complete skill set in one PDF.

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  • FORMATPDF + ATS .txt
  • UPDATED2026 · Q4
  • ROLES3 · 4+ years
  • PROJECTS3 · AI/ML
  • EDUM.S. BA · UAlbany

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Shreyas Mysore Sudesh — Data Engineer

Shreyas Mysore Sudesh

Data Engineer

United States

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