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
