Posted:1 day ago|
Platform:
On-site
Full Time
Job Title: AI Engineer Experience: 6-12 Years Location: Bangalore, Gurugram (Work From Office – 5 Days) Employment Type: Full-Time Interested Candidates Apply Here :- https://forms.gle/h3pXgxc57kUB3UJX6 Job Description :We are looking for a seasoned AI Enginee r with experienc e in designing and deploying AI/ML solutions, with a strong focus on Generative AI (GenAI ) and Large Language Models (LLMs ). The ideal candidate should have deep expertise in Pytho n, machine learnin g, and artificial intelligenc e, along with a strong understanding of real-world AI productization .As part of our advanced tech team in Bangalor e, you will play a critical role in building and integrating AI-powered applications that solve real business problems .Key Responsibilities :Design, develop, and deploy AI/ML solutions with a focus on Generative A I and LLM s (e.g., GPT, BERT, Claude, etc. )Fine-tune and customize foundation models for specific domain or business requirement sDevelop intelligent systems using Pytho n and modern ML framework sCollaborate with cross-functional teams including product, data engineering, and design to integrate AI into core product sEvaluate, test, and implement third-party APIs, libraries, and models relevant to AI/ML/LLM task sOptimize model performance, latency, and scalability for production us eStay up to date with the latest advancements in AI, machine learning, and GenAI technologie sRequired Skills :9+ years of professional experience in AI/M L role sStrong expertise in Generative A I, LLM s, and natural language processing (NLP )Proficient in Pytho n and libraries like TensorFlow, PyTorch, Hugging Face Transformer sSolid background in machine learning algorithm s, deep learning, and model evaluation technique sExperience deploying and maintaining ML models in productio nStrong problem-solving skills and ability to handle ambiguity in requirement sGood to Have :Experience with prompt engineering and few-shot/fine-tuning technique sKnowledge of MLOps practices and tools (e.g., MLflow, Kubeflow, Airflow )Familiarity with vector databases and retrieval-augmented generation (RAG) pipeline sCloud experience (AWS, GCP, or Azure ) Show more Show less
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