Data Science -AI/ML

Desired Competencies (Technical/Behavioral Competency)

Must-Have

• Experience in developing and deploying GenAI/LLM powered applications/products
• Experience in building Agentic AI systems, including planning, reasoning, and decision-making components.
• Required proficiency in Python and relevant AI/ML libraries (e.g., TensorFlow, PyTorch, transformers, LangChain, LangGraph, Autogen, LLamaIndex etc.).
• Required experience with Natural Language Processing (NLP) techniques, including text generation, understanding, and summarization.
• Proficiency in Python and common ML/NLP libraries (e.g., scikit-learn, spaCy, Hugging Face Transformers).
• Hands-on experience with anomaly detection techniques such as Isolation Forest, One-Class SVM, Autoencoders, or statistical methods.
• Familiarity with NLP tasks such as classification, summarization, and named entity recognition.
• Experience with vectorization techniques (TF-IDF, Word2Vec, BERT, etc.).
• Experience with vector databases (e.g., FAISS, Pinecone, ChromaDB).
• Exposure to LLMs and prompt engineering.
  
  

Good-to-Have

• Preferred experience with prompt engineering and fine-tuning large language models.
• Preferred experience with knowledge graphs and semantic reasoning.
• Preferred experience with multi-agent systems and their coordination.
• Preferred experience with explainable AI (XAI) techniques.
• Preferred experience with MLOps and model deployment pipelines.
• Experience with LangChain or Retrieval-Augmented Generation (RAG) pipelines
• Familiarity with embedding strategies and chunking techniques
• Exposure to LLMOps tools and frameworks
• Understanding of Responsible AI principles and ethical AI development
  
  

SN

Responsibility of / Expectations from the Role

1

Design and implement machine learning models for anomaly detection in time series and behavioral data.

2

Develop and maintain NLP pipelines for document processing and content generation.

3

Preprocess and clean structured and unstructured data using standard techniques.

4

Implement vectorization techniques and integrate with vector databases (e.g., FAISS, Pinecone, MongoDB Atlas Vector).

5

Work with embedding models (e.g., OpenAI, Hugging Face) to support semantic search and retrieval tasks.

6

Fine-tune and evaluate LLMs for specific use cases such as summarization, classification, and test case generation.

7

Collaborate with backend engineers to expose ML models via APIs.

8

Monitor model performance using metrics like precision, recall, F1 score, and ROC-AUC.

9

Contribute to proof-of-concept projects involving GenAI and RAG architectures.

10

Follow Responsible AI practices in model development and deployment.

Desired Competencies (Technical/Behavioral Competency)

Must-Have

• Experience in developing and deploying GenAI/LLM powered applications/products
• Experience in building Agentic AI systems, including planning, reasoning, and decision-making components.
• Required proficiency in Python and relevant AI/ML libraries (e.g., TensorFlow, PyTorch, transformers, LangChain, LangGraph, Autogen, LLamaIndex etc.).
• Required experience with Natural Language Processing (NLP) techniques, including text generation, understanding, and summarization.
• Proficiency in Python and common ML/NLP libraries (e.g., scikit-learn, spaCy, Hugging Face Transformers).
• Hands-on experience with anomaly detection techniques such as Isolation Forest, One-Class SVM, Autoencoders, or statistical methods.
• Familiarity with NLP tasks such as classification, summarization, and named entity recognition.
• Experience with vectorization techniques (TF-IDF, Word2Vec, BERT, etc.).
• Experience with vector databases (e.g., FAISS, Pinecone, ChromaDB).
• Exposure to LLMs and prompt engineering.
  
  

Good-to-Have

• Preferred experience with prompt engineering and fine-tuning large language models.
• Preferred experience with knowledge graphs and semantic reasoning.
• Preferred experience with multi-agent systems and their coordination.
• Preferred experience with explainable AI (XAI) techniques.
• Preferred experience with MLOps and model deployment pipelines.
• Experience with LangChain or Retrieval-Augmented Generation (RAG) pipelines
• Familiarity with embedding strategies and chunking techniques
• Exposure to LLMOps tools and frameworks
• Understanding of Responsible AI principles and ethical AI development
  
  

SN

Responsibility of / Expectations from the Role

1

Design and implement machine learning models for anomaly detection in time series and behavioral data.

2

Develop and maintain NLP pipelines for document processing and content generation.

3

Preprocess and clean structured and unstructured data using standard techniques.

4

Implement vectorization techniques and integrate with vector databases (e.g., FAISS, Pinecone, MongoDB Atlas Vector).

5

Work with embedding models (e.g., OpenAI, Hugging Face) to support semantic search and retrieval tasks.

6

Fine-tune and evaluate LLMs for specific use cases such as summarization, classification, and test case generation.

7

Collaborate with backend engineers to expose ML models via APIs.

8

Monitor model performance using metrics like precision, recall, F1 score, and ROC-AUC.

9

Contribute to proof-of-concept projects involving GenAI and RAG architectures.

10

Follow Responsible AI practices in model development and deployment.

Desired Competencies (Technical/Behavioral Competency)

Must-Have

• Experience in developing and deploying GenAI/LLM powered applications/products
• Experience in building Agentic AI systems, including planning, reasoning, and decision-making components.
• Required proficiency in Python and relevant AI/ML libraries (e.g., TensorFlow, PyTorch, transformers, LangChain, LangGraph, Autogen, LLamaIndex etc.).
• Required experience with Natural Language Processing (NLP) techniques, including text generation, understanding, and summarization.
• Proficiency in Python and common ML/NLP libraries (e.g., scikit-learn, spaCy, Hugging Face Transformers).
• Hands-on experience with anomaly detection techniques such as Isolation Forest, One-Class SVM, Autoencoders, or statistical methods.
• Familiarity with NLP tasks such as classification, summarization, and named entity recognition.
• Experience with vectorization techniques (TF-IDF, Word2Vec, BERT, etc.).
• Experience with vector databases (e.g., FAISS, Pinecone, ChromaDB).
• Exposure to LLMs and prompt engineering.
  
  

Good-to-Have

• Preferred experience with prompt engineering and fine-tuning large language models.
• Preferred experience with knowledge graphs and semantic reasoning.
• Preferred experience with multi-agent systems and their coordination.
• Preferred experience with explainable AI (XAI) techniques.
• Preferred experience with MLOps and model deployment pipelines.
• Experience with LangChain or Retrieval-Augmented Generation (RAG) pipelines
• Familiarity with embedding strategies and chunking techniques
• Exposure to LLMOps tools and frameworks
• Understanding of Responsible AI principles and ethical AI development
  
  

SN

Responsibility of / Expectations from the Role

1

Design and implement machine learning models for anomaly detection in time series and behavioral data.

2

Develop and maintain NLP pipelines for document processing and content generation.

3

Preprocess and clean structured and unstructured data using standard techniques.

4

Implement vectorization techniques and integrate with vector databases (e.g., FAISS, Pinecone, MongoDB Atlas Vector).

5

Work with embedding models (e.g., OpenAI, Hugging Face) to support semantic search and retrieval tasks.

6

Fine-tune and evaluate LLMs for specific use cases such as summarization, classification, and test case generation.

7

Collaborate with backend engineers to expose ML models via APIs.

8

Monitor model performance using metrics like precision, recall, F1 score, and ROC-AUC.

9

Contribute to proof-of-concept projects involving GenAI and RAG architectures.

10

Follow Responsible AI practices in model development and deployment.

Desired Competencies (Technical/Behavioral Competency)

Must-Have

• Experience in developing and deploying GenAI/LLM powered applications/products
• Experience in building Agentic AI systems, including planning, reasoning, and decision-making components.
• Required proficiency in Python and relevant AI/ML libraries (e.g., TensorFlow, PyTorch, transformers, LangChain, LangGraph, Autogen, LLamaIndex etc.).
• Required experience with Natural Language Processing (NLP) techniques, including text generation, understanding, and summarization.
• Proficiency in Python and common ML/NLP libraries (e.g., scikit-learn, spaCy, Hugging Face Transformers).
• Hands-on experience with anomaly detection techniques such as Isolation Forest, One-Class SVM, Autoencoders, or statistical methods.
• Familiarity with NLP tasks such as classification, summarization, and named entity recognition.
• Experience with vectorization techniques (TF-IDF, Word2Vec, BERT, etc.).
• Experience with vector databases (e.g., FAISS, Pinecone, ChromaDB).
• Exposure to LLMs and prompt engineering.
  
  

Good-to-Have

• Preferred experience with prompt engineering and fine-tuning large language models.
• Preferred experience with knowledge graphs and semantic reasoning.
• Preferred experience with multi-agent systems and their coordination.
• Preferred experience with explainable AI (XAI) techniques.
• Preferred experience with MLOps and model deployment pipelines.
• Experience with LangChain or Retrieval-Augmented Generation (RAG) pipelines
• Familiarity with embedding strategies and chunking techniques
• Exposure to LLMOps tools and frameworks
• Understanding of Responsible AI principles and ethical AI development
  
  

SN

Responsibility of / Expectations from the Role

1

Design and implement machine learning models for anomaly detection in time series and behavioral data.

2

Develop and maintain NLP pipelines for document processing and content generation.

3

Preprocess and clean structured and unstructured data using standard techniques.

4

Implement vectorization techniques and integrate with vector databases (e.g., FAISS, Pinecone, MongoDB Atlas Vector).

5

Work with embedding models (e.g., OpenAI, Hugging Face) to support semantic search and retrieval tasks.

6

Fine-tune and evaluate LLMs for specific use cases such as summarization, classification, and test case generation.

7

Collaborate with backend engineers to expose ML models via APIs.

8

Monitor model performance using metrics like precision, recall, F1 score, and ROC-AUC.

9

Contribute to proof-of-concept projects involving GenAI and RAG architectures.

10

Follow Responsible AI practices in model development and deployment.

Skills: langchain,generative ai,python,ai/ml