Qode
Key Responsibilities
Build and optimize
classification, regression , and
forecasting models
using classical ML and deep learning techniques. Develop and deploy
deep learning architectures
including
LSTMs ,
transformers , and other sequence-based models for time-series, NLP, and anomaly detection. Design and implement
NLP pipelines
for text classification, semantic search, summarization, and question answering using transformer-based models (e.g., BERT, T5, GPT). Create
RAG (retrieval-augmented generation) pipelines
integrating LLMs with vector databases (e.g., FAISS, Pinecone, Weaviate) and document indexing frameworks. Apply and fine-tune
LLMs
(e.g., OpenAI, Mistral, LLaMA, Cohere) for domain-specific tasks using supervised fine-tuning or LoRA/QLoRA methods. Build and orchestrate
multi-agent AI systems
using frameworks like
LangGraph , CrewAI, or OpenAgents to support tool-using, autonomous agents for decision-making workflows. Collaborate with data engineers, product managers, and stakeholders to translate business needs into production-ready solutions. Mentor and support junior data scientists through code reviews, model design feedback, and collaborative experimentation. Promote best practices in reproducible modeling, responsible AI, and scalable deployment. Required Skills & Experience 5+ years of experience in data science or applied machine learning, with a strong background in both classical and deep learning methods. Hands-on experience with
Python , and libraries/frameworks such as scikit-learn, pandas, PyTorch, TensorFlow, Hugging Face Transformers, and LangChain. Strong understanding of
classification metrics , feature engineering, model validation, and hyperparameter tuning. Demonstrated experience with
LLMs , including fine-tuning, prompt engineering, and
retrieval-augmented generation
techniques. Familiarity with
vector databases , embedding models, and chunking strategies for unstructured data (e.g., PDFs, knowledge bases). Experience working with
multi-agent architectures
or orchestration tools like
LangGraph , CrewAI, or AutoGPT. Solid skills in
data analysis, visualization , and communicating technical insights clearly to mixed audiences. Knowledge of cloud platforms (AWS/GCP/Azure) and deployment tools (e.g., Docker, MLflow, FastAPI).
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classification, regression , and
forecasting models
using classical ML and deep learning techniques. Develop and deploy
deep learning architectures
including
LSTMs ,
transformers , and other sequence-based models for time-series, NLP, and anomaly detection. Design and implement
NLP pipelines
for text classification, semantic search, summarization, and question answering using transformer-based models (e.g., BERT, T5, GPT). Create
RAG (retrieval-augmented generation) pipelines
integrating LLMs with vector databases (e.g., FAISS, Pinecone, Weaviate) and document indexing frameworks. Apply and fine-tune
LLMs
(e.g., OpenAI, Mistral, LLaMA, Cohere) for domain-specific tasks using supervised fine-tuning or LoRA/QLoRA methods. Build and orchestrate
multi-agent AI systems
using frameworks like
LangGraph , CrewAI, or OpenAgents to support tool-using, autonomous agents for decision-making workflows. Collaborate with data engineers, product managers, and stakeholders to translate business needs into production-ready solutions. Mentor and support junior data scientists through code reviews, model design feedback, and collaborative experimentation. Promote best practices in reproducible modeling, responsible AI, and scalable deployment. Required Skills & Experience 5+ years of experience in data science or applied machine learning, with a strong background in both classical and deep learning methods. Hands-on experience with
Python , and libraries/frameworks such as scikit-learn, pandas, PyTorch, TensorFlow, Hugging Face Transformers, and LangChain. Strong understanding of
classification metrics , feature engineering, model validation, and hyperparameter tuning. Demonstrated experience with
LLMs , including fine-tuning, prompt engineering, and
retrieval-augmented generation
techniques. Familiarity with
vector databases , embedding models, and chunking strategies for unstructured data (e.g., PDFs, knowledge bases). Experience working with
multi-agent architectures
or orchestration tools like
LangGraph , CrewAI, or AutoGPT. Solid skills in
data analysis, visualization , and communicating technical insights clearly to mixed audiences. Knowledge of cloud platforms (AWS/GCP/Azure) and deployment tools (e.g., Docker, MLflow, FastAPI).
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