Job Description
Role & Responsibilities
Design and implement classic ML models (XGBoost, SVMs, clustering, etc.) for structured data use cases.
Build and optimize deep learning pipelines using Py Torch/Tensor Flow, with a focus on NLP and multimodal tasks.
Develop modern AI systems , including RAG-based applications leveraging Lang Chain, Llama Index, and vector databases (FAISS, Weaviate).
Architect and experiment with agentic AI systems featuring tool usage, memory, planning, and self-reflection (Crew AI, Lang Graph, Auto Gen).
Deploy ML models in production environments using Docker, MLflow, Ray, or Kubernetes.
Preferred Candidate Profile
3–5 years of experience in software or ML engineering.
Strong proficiency in Python , with hands-on experience in ML model building.
Exposure to open-source LLMs (e.g., LLa MA, Mistral, Falcon).
Bonus: Experience in fine-tuning LLMs or building autonomous AI agents.
Key Attributes
Strong problem-solving and analytical skills.
Design and implement classic ML models (XGBoost, SVMs, clustering, etc.) for structured data use cases.
Build and optimize deep learning pipelines using Py Torch/Tensor Flow, with a focus on NLP and multimodal tasks.
Develop modern AI systems , including RAG-based applications leveraging Lang Chain, Llama Index, and vector databases (FAISS, Weaviate).
Architect and experiment with agentic AI systems featuring tool usage, memory, planning, and self-reflection (Crew AI, Lang Graph, Auto Gen).
Deploy ML models in production environments using Docker, MLflow, Ray, or Kubernetes.
Preferred Candidate Profile
3–5 years of experience in software or ML engineering.
Strong proficiency in Python , with hands-on experience in ML model building.
Exposure to open-source LLMs (e.g., LLa MA, Mistral, Falcon).
Bonus: Experience in fine-tuning LLMs or building autonomous AI agents.
Key Attributes
Strong problem-solving and analytical skills.
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