Devmallya Karar
AI Engineer
7+ years ML exp
Management: 3-5 years
Python
SQL
Model Architecture & Training: 4/10
Data Pipeline & Feature Engineering: 4/10
Experimentation & Evaluation: 4/10
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Overview
Technical skills
Timeline
Roles
Overview
Applied ML engineer at a middle level specializing in fraud and anomaly-detection pipelines with a strength in end-to-end model development for imbalanced datasets. The strongest proven skill is hands-on imbalance handling and model evaluation, demonstrated by multiple notebooks that implement oversampling/undersampling (SMOTE/ADASYN/RandomOverSampler/RandomUnderSampler), systematic GridSearchCV experiments and threshold/ROC analysis. What is not evidenced is production-grade engineering - there is little modular production code, no CI/experiment tracking, serving or monitoring infrastructure in the public artifacts.
Phone
Technical skills
Languages
2
Python
SQL
Python
3
FastAPI
Django
Pydantic
AI/ML
34
LLM
XGBoost
TensorFlow
BERT
Vertex AI
ResNet
PyTorch
Claude
AWS Bedrock
LangChain
LangGraph
Mistral
Computer Vision
Langfuse
LangSmith
Model Context Protocol
Fine-tuning
NER
Reinforcement Learning
NumPy
Pandas
Scikit-learn
YOLO
RAG
Keras
TF-Keras
Statsmodels
LightGBM
CrewAI
Transformers
Time Series Forecasting
Torchvision
Jupyter Notebook
Few-Shot Learning
DevOps
7
AWS
GCP
Rest API
CI/CD
Azure
Heroku
Docker
Other
15
Postman
Matplotlib
Seaborn
Claude Code
Cursor
Cline
Deep Learning
Embeddings
Prompt Engineering
GDPR
NLP
Vector
PID Control
CNN
Hallucination
Timeline
Lead AI Engineer
•
Lead
Bayer AG
•
Full-Time
Owned the end-to-end architecture of Bayer’s enterprise machine-translation platform, including domain-aware routing and speech-to-text/text-to-speech capabilities. Designed an agentic conversational system that combines retrieval and multi-agent collaboration with real-time streaming. Built and orchestrated multiple production agents on AWS using LangGraph/LangChain, including secure tool-discovery via MCP and OAuth-based access control, with human approval checkpoints for sensitive actions. Implemented LLM evaluation and observability with LangSmith and Langfuse based on live tool-call traces, and delivered a camera translation feature using OCR and LLMs.
AWS
LangGraph
LangChain
RAG
Model Context Protocol
LangSmith
Langfuse
LLM
Lead AI Consultant
•
Lead
HCLTech
•
Full-Time
Delivered a first-generation domain-adaptive machine-translation system for Bayer using LangChain/LangGraph agents, RAG, and routing across multiple engines. Implemented an AI-generated-text detection and blocking layer using Amazon Bedrock (Claude) with few-shot prompting, reducing non-compliant content by the stated amount. Built a PII-safe medical document parser on AWS using Bedrock and FastAPI, applying RAG chunking for field extraction under healthcare data-protection constraints. Developed email intent classification with BERT-based models and AWS services, and built response generation workflows using Bedrock models with PyTorch and LangChain.
AWS Bedrock
AWS
LangGraph
LangChain
RAG
BERT
FastAPI
Few-Shot Learning
PyTorch
LLM
Claude
Mistral
AI/ML Engineer - Lead Assistant Manager
•
Lead
EXL
•
Full-Time
Led a team of ML engineers and data scientists delivering AI, computer-vision, and NLP products across multiple insurance and financial-services clients. Built document-oriented GenAI solutions for summarization and key-value extraction, taking multiple solutions from proof of concept to production. Fine-tuned LLM-based extractors and improved extraction quality using BERT-enhanced approaches, leveraging GCP for model workflows. Implemented a Mask R-CNN pipeline for extracting information from embedded charts and mentored junior data scientists.
Fine-tuning
LLM
BERT
GCP
Computer Vision
Liverpool John Moores University
Master's Degree •
Machine Learning and Artificial Intelligence
Machine Learning Engineer
•
Middle
Quantiphi
•
Full-Time
Shipped a DistilBERT-based named-entity recognition model for medical entities extracted from insurance-claim forms. Developed ensemble classifiers over clinical text entities using XGBoost to improve classification quality versus a single-model baseline. Built BERT-NER proofs of concept for document extraction using AWS Textract and document-layout approaches. Delivered a real-time medical-invoice table detection model on GCP Vertex AI with low per-page latency.
BERT
NER
XGBoost
AWS
Vertex AI
Computer Vision
Data Scientist - Associate Product Development
•
Middle
HARMAN International
•
Full-Time
Designed multivariate deep-learning forecasting models in TensorFlow to improve sales forecast accuracy versus a statistical baseline. Built a 3D hand-gesture recognition system for an automotive OEM using a ResNet architecture and deployed it on-device with TensorFlow Lite on GCP. Created reproducible CI/CD pipelines for scalable model deployment, reducing release cycle time. Performed analysis on high-dimensional socio-economic data using GIS spatial-classification techniques to support product targeting decisions.
TensorFlow
ResNet
CI/CD
Time Series Forecasting
Computer Vision
Vertex AI
Software Engineer
•
Middle
Hewlett-Packard
•
Full-Time
Developed and deployed a collaborative-filtering recommendation system for HP’s e-commerce platform to improve click-through behavior. Built a customer-support chatbot using Rasa with Python to automate routine queries and reduce tier-1 support load. Deployed ML models to production using Django along with GCP Vertex AI to serve live traffic on the e-commerce site. Validated API performance and reliability with Postman across many model endpoints before release.
Vertex AI
Postman
Rest API
Middle AI/ML Engineer
Confidence: Medium ML Engineer
Applied ML engineer at a middle level specializing in fraud and anomaly-detection pipelines with a strength in end-to-end model development for imbalanced datasets. The strongest proven skill is hands-on imbalance handling and model evaluation, demonstrated by multiple notebooks that implement oversampling/undersampling (SMOTE/ADASYN/RandomOverSampler/RandomUnderSampler), systematic GridSearchCV experiments and threshold/ROC analysis. What is not evidenced is production-grade engineering - there is little modular production code, no CI/experiment tracking, serving or monitoring infrastructure in the public artifacts.
Model Architecture & Training
4/10
How well models are designed and trained
Practical model-building across classical ML, gradient-boosted trees and neural nets; shows end-to-end training, hyperparameter search and a custom RL network and training loop but no novel research-level architectures or extensive optimizer/schedule engineering.
Evidence
Credit_Card_Fraud_Detection_DL_1.ipynb: autoencoder model definition (input_layer -> encoder -> decoder -> autoencoder) with callbacks and training loop
Credit_card_fraud_Devmallya_Karar (2).ipynb: extensive GridSearchCV hyperparameter tuning for RandomForest/XGBoost/KNN across sampling strategies
Credit_Card_Fraud_Detection_via_DQNs.ipynb: class DQN and Agent.learn custom training loop with soft_update and replay buffer
Data Pipeline & Feature Engineering
4/10
How data is prepared for models
Solid applied data pipeline and imbalance handling work: scaling, power-transform, many sampling strategies (oversampling/undersampling/SMOTE/ADASYN) and feature analysis; pipelines are notebook-driven rather than production ETL.
Evidence
Credit_card_fraud_Devmallya_Karar (2).ipynb: PowerTransformer usage (PowerTransformer(method='yeo-johnson')) and creation of X_train_transform/X_test_transform
Credit_card_fraud_Devmallya_Karar (2).ipynb: imbalanced-learn sampling pipelines (RandomOverSampler, SMOTE, ADASYN, RandomUnderSampler) applied before model training
Time Series forecasting to predict the volumes Final.ipynb: stationarity tests and SARIMAX model selection functions (adfuller_test, best_sarima_model)
Experimentation & Evaluation
4/10
How results are measured and tested
Reasonable experimentation and evaluation practices: stratified CV, GridSearchCV, custom thresholding/ROC analysis, confusion matrices and SHAP for feature importance; missing formal experiment tracking and reproducible config files.
Evidence
Credit_card_fraud_Devmallya_Karar (2).ipynb: repeated use of GridSearchCV with StratifiedKFold across models and sampling variants (e.g., randomforest_model_2, xgboost_model_1)
Credit_card_fraud_Devmallya_Karar (2).ipynb: optimal_cutoff and draw_roc functions used to select thresholds and plot ROC/metrics
Credit_Card_Fraud_Detection_DL_1.ipynb: shap.TreeExplainer and shap.summary_plot to inspect model feature importances
MLOps & Deployment
2/10
How models are shipped to production
Minimal MLOps/serving evidence: model checkpointing and saving are used, and there are notes about Kaggle/Colab commands; lacks deployment/service code, CI/CD, monitoring or model versioning.
Evidence
Credit_Card_Fraud_Detection_DL_1.ipynb: tf.keras.callbacks.ModelCheckpoint(filepath='best_model.h5') and saving/loading best_model.h5
Credit_Card_Fraud_Detection_DL_1.ipynb: use of kaggle CLI and Colab setup commands in notebook for data download
Computational Efficiency
2/10
How efficiently computing resources are used
Some awareness of computational choices (GPU-enabled XGBoost, torch.device selection) but no measured efficiency work, profiling, batching studies or quantization.
Evidence
Credit_card_fraud_Devmallya_Karar (2).ipynb: XGBClassifier(..., tree_method='gpu_hist') indicating GPU usage for training
Credit_Card_Fraud_Detection_via_DQNs.ipynb: device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') and .to(device) usage for networks/tensors
Research Depth & Innovation
2/10
Depth of research and new ideas
Applied implementations of known methods (autoencoder anomaly detection, DQN agent) but no original algorithmic contributions or research-grade reproductions with ablations and published comparisons.
Evidence
Credit_Card_Fraud_Detection_DL_1.ipynb: autoencoder-based anomaly detection pipeline with threshold selection and evaluation
Credit_Card_Fraud_Detection_via_DQNs.ipynb: DQN class, ReplayBuffer and Agent.learn custom RL loop
Expertise
AI Agents & Agentic Workflows• Middle
MLOps & Model Lifecycle• Junior
Technologies
Python• Middle
SQL
Cursor
LangGraph• since 2024
Rest API• since 2018
LangChain• since 2024
Claude• since 2024
GCP• since 2022
Claude Code
Model Context Protocol• since 2025
XGBoost• since 2021
Vertex AI• since 2021
FastAPI• since 2024
Fine-tuning• since 2022
Embeddings
Reinforcement Learning• since 2022
Prompt Engineering
Computer Vision• since 2019
NLP
NER• since 2021
Cline
Langfuse• since 2025
LangSmith• since 2025
Heroku
Azure
Mistral• since 2024
AWS Bedrock• since 2024
CI/CD• since 2019
Transformers
TensorFlow• since 2019
Django
PyTorch• since 2024
AWS• since 2021
Docker
CrewAI
LLM• since 2022
RAG• since 2024
BERT• since 2021
ResNet• since 2019
Pydantic
Torchvision
Vector
Few-Shot Learning• since 2024
Hallucination
CNN
Time Series Forecasting• since 2019
Recommendations
- Refactor notebooks into modular Python packages and reusable modules (data preprocessing, model training, evaluation) with unit tests and clear entry points for productionization.
- Add experiment tracking and reproducibility (W&B/MLflow runs, fixed config files, seed control) and a CI pipeline to validate models and training scripts.
- Implement a lightweight serving prototype (FastAPI or KServe/BentoML) and basic monitoring (metrics, drift checks) for the best-performing model to validate production constraints.
- Harden the RL work by adding stable training loops, evaluation episodes, deterministic seeds and clearer reward/episode logging; consider leveraging stable-baselines3 or Ray RLlib for scale and reproducibility.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Middle Data Scientist
Confidence: Medium ML Practitioner
A middle-level ML practitioner focused on applied predictive modeling and time-series forecasting with strong hands-on experience. The strongest proven skill is end-to-end model development and tuning for time-series and tabular problems, demonstrated by SARIMA-based forecasting pipelines and large-scale classification work with hyperparameter search and tree-ensemble models. Not evidenced are production engineering practices such as modularized reusable code, environment pinning, CI/CD, automated tests, and data/version control.
Statistical Rigor
5/10
Correct use of statistics
Uses appropriate statistical tests and discusses assumptions (ADF, Shapiro, decomposition and normality) but uncertainty quantification is limited and there are occasional metric/method misuse issues.
Evidence
Time-Series-forecasting-to-predict-the-volumes/Time Series forecasting to predict the volumes Final.ipynb: test_stationarity / adfuller_test / shapiro_normality_test
Classification-Problem---Insurance/Classification Problem - Insurance Devmallya Karar (2).ipynb: Central Limit Theorem and confidence interval calculations
Data Wrangling & Cleaning
6/10
Preparing and cleaning data
Solid practical data wrangling: missing-value handling, outlier removal, encoding and scaling are implemented; some operations are notebook-bound with hard-coded local paths and potential reproducibility/leakage risks.
Evidence
Classification-Problem---Insurance/Classification Problem - Insurance Devmallya Karar (2).ipynb: outlier removal (IQR) and get_dummies / StandardScaler pipelines
Time-Series-forecasting-to-predict-the-volumes/Time Series forecasting to predict the volumes Final.ipynb: month-name to numeric conversion and datetime index setup
Exploratory Analysis & Visualization
6/10
Exploring and visualizing data
Exploratory analysis is thorough and accompanied by written interpretation (seasonality, ACF/PACF, distributions, class counts); visualizations are used to motivate modeling choices.
Evidence
Time-Series-forecasting-to-predict-the-volumes/Time Series forecasting to predict the volumes Final.ipynb: plot_data_properties, seasonal_decompose and year-by-year monthly plots with commentary
Classification-Problem---Insurance/Classification Problem - Insurance Devmallya Karar (2).ipynb: extensive countplots, distribution plots and grouped analyses with text explanations
Predictive Modeling
5/10
Building models that predict
Good breadth of predictive modeling (SARIMA/SARIMAX, ARIMA, LSTM, logistic, tree ensembles, XGBoost, LightGBM) and hyperparameter search (RandomizedSearchCV, hyperopt), but evaluation hygiene and production-readiness (robust error analysis, calibration, clear CV pipelines) are uneven.
Evidence
Time-Series-forecasting-to-predict-the-volumes/Time Series forecasting to predict the volumes Final.ipynb: best_sarima_model grid-search function and SARIMAX forecasting pipeline
Classification-Problem---Insurance/Classification Problem - Insurance Devmallya Karar (2).ipynb: hyperopt objective, RandomizedSearchCV for RandomForest, XGBoost and LightGBM training and model saving
Stock-price-Prediction-using-LSTM/Stock price Prediction using LSTM.ipynb: LSTM model definition, scaling, training and prediction pipeline
Business Insight & Impact
3/10
Turning analysis into business value
Some problem framing and discussion of precision-recall tradeoffs for the insurance use-case, but limited explicit business impact quantification, cost-of-error analysis or deployment/operational considerations.
Evidence
Classification-Problem---Insurance/Classification Problem - Insurance Devmallya Karar (2).ipynb: Problem Statement framing for an insurance client and discussion of precision-recall tradeoffs in model selection
Time-Series-forecasting-to-predict-the-volumes/Time Series forecasting to predict the volumes Final.ipynb: explanation of MAPE and selection rationale
Reproducibility & Notebook Hygiene
2/10
Clean, repeatable analysis
Work is notebook-first and reproducible for exploration but lacks production hygiene: hard-coded local paths, no environment pin files, minimal modularization, and only ad-hoc model pickling.
Evidence
Multiple notebooks (e.g. Time Series forecasting to predict the volumes Final.ipynb, Classification Problem - Insurance Devmallya Karar (2).ipynb) use absolute local file paths like 'E:/PIP_Devmallya/...' and save artifacts to local paths
Classification-Problem---Insurance/Classification Problem - Insurance Devmallya Karar (2).ipynb: model saved via pickle but no requirements/conda/Docker or data versioning present; presence of random_state in train_test_split shows partial reproducibility effort
Industries
Commerce• Middle
Financial Services• Middle
Technologies
Deep Learning
Jupyter Notebook
YOLO
Scikit-learn
Seaborn
Matplotlib
LightGBM
Pandas
NumPy
Keras
TF-Keras
Statsmodels
Recommendations
- Develop prototype production pipelines: refactor notebook code into modular Python packages, add unit tests, and adopt CI to make models deployable.
- Add reproducibility artifacts: provide requirements/conda files, container (Docker) images and remove hard-coded local paths so colleagues can re-run and deploy.
- Focus on MLOps and monitoring: build model serving, scheduling (Airflow/Dagster) and post-deployment monitoring for forecasts and classifiers.
- Improve evaluation rigor: add proper probability calibration, class-imbalance handling, robust cross-validation schemes and clearer business-cost/error analyses.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories: