Key Responsibilities:
- Build and optimize AI/ML pipelines for predictive modeling, NLP, and generative AI applications.
- Perform Exploratory Data Analysis (EDA), data mining, and visualization to extract insights.
- Design and implement Big Data solutions using Hadoop, Spark, PySpark, and DataLake architectures.
- Develop and deploy ML models (supervised, unsupervised, tree-based, ensemble methods).
- Implement deep learning architectures (CNN, RNN, LSTM) using TensorFlow, PyTorch, and Keras.
- Work on NLP and Generative AI tasks including embeddings, transformers (BERT, GPT), and OpenAI APIs.
- Integrate agentic AI frameworks (LangChain, LangGraph, MCP, Bedrock Agents) for autonomous workflows.
- Collaborate with cross-functional teams to deploy AI solutions in production environments.
- Ensure scalability, security, and compliance of AI systems.


Required Skills:

Analytical Tools:
- EDA, Data Mining, Visualization (Plotly, Matplotlib, Seaborn)
- Statistical & Multivariate Analysis

Big Data:
- Hadoop, MapReduce, HDFS, DataBricks, Spark, PySpark
- DataLake Architecture, AWS Redshift, Kinesis, EMR

Machine Learning
- Supervised Models: Nave Bayes, Logistic Regression, SVM, Linear Regression, KNN
- Tree-Based Models: Decision Trees, Random Forest, Gradient Boosted Trees, XGBoost
- Unsupervised Models: K-Means, DBSCAN, Hierarchical Clustering

Deep Learning
- ANN, CNN, RNN, LSTM
- Frameworks: TensorFlow (Gradient Tape), PyTorch NN, Keras Sequential

NLP & Generative AI
- NLTK, CBoW, n-grams, Word2Vec, TF-IDF, Word Embeddings
- Transformers: BERT, ELMo
- OpenAI Models: GPT-3.5 Turbo, GPT-4o, GPT-3o Reasoning, text-embedding-ada-002

Libraries & Tools
- numpy, pandas, scipy, scikit-learn, tensorflow, keras, nltk, matplotlib, seaborn, plotly

Programming Languages
- Python, R, C++, C#, Java, Node.js, HTML, SQL

Agentic AI
- OpenAI Agents SDK, Model Context Protocol (MCP), LangChain, LangGraph, Bedrock Agents, CrewAI, Helicone