Live opening · Posted 8 hours ago

AI/ML Engineer Walk-in drive

Tata Consultancy Services (TCS) · Hyderabad, Telangana, India (On-site)
Linkedin No
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At a glance

The key details from the original listing.

Posted 8 hours ago
CompanyTata Consultancy Services (TCS)
LocationHyderabad, Telangana, India (On-site)
Work modeNo
SkillsPython, FastAPI, Flask, AWS, Azure, Docker, Kubernetes
SourceLinkedin
ListedPosted 8 hours ago

About the role

Description supplied by the original job listing.

Walk-in drive at TCS Hyderabad office on 31st October:::AI/ML Engineer:
Must Have:Experience in working with various ML libraries and packages like Scikit learn, Numpy, Pandas, Tensorflow, Matplotlib, Caffe, etc.Deep Learning Frameworks: PyTorch, spaCy, KerasDeep Learning Architectures: LSTM, CNN, Self-Attention and TransformersExperience in working with Image processing, computer vision is mustDesigning data science applications, Large Language Models(LLM) , Generative Pre-trained Transformers (GPT), generative AI techniques, Natural Language Processing (NLP), machine learning techniques, Python, Jupyter Notebook, common data science packages (tensorflow, scikit-learn,keras etc.,.) , LangChain, Flask, FastAPI, prompt engineering.Programming experience in PythonStrong written and verbal communicationsExcellent interpersonal and collaboration skills.
Good-to-Have:Experience working with vectored databases and graph representation of documents.Experience with building or maintaining MLOps pipelines.Experience in Cloud computing infrastructures like AWS Sagemaker or Azure ML for implementing ML solutions is preferred.Exposure to Docker, Kubernetes
Expectations from the Role:1.Design and implement scalable and efficient data architectures to support generative AI workflows.2.Fine tune and optimize large language models (LLM) for generative AI, conduct performance evaluation and benchmarking for LLMs and machine learning models3.Apply prompt engineer techniques as required by the use case4.Collaborate with research and development teams to build large language models for generative AI use cases, plan and breakdown of larger data science tasks to lower-level tasks5.Lead junior data engineers on tasks such as design data pipelines, dataset creation, and deployment, use data visualization tools, machine learning techniques, natural language processing , feature engineering, deep learning , statistical modelling as required by the use case.

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