Machine Learning Engineer - Deep Learning / Computer Vision

Role : Machine Learning Engineer / Architect

Required Technical Skill Set skill : Python

Secondary skill : AWS,Azure / GCP

Desired Competencies (Technical / Behavioral Competency) :

Must-Have :

1. Experience in working with various ML libraries and packages like Scikit learn, Numpy, Pandas, Tensorflow, Matplotlib, Caffe, etc.

2. Deep Learning Frameworks : PyTorch, spaCy, Keras

3. Deep Learning Architectures : LSTM, CNN, Self-Attention and Transformers

4. Experience in working with Image processing, computer vision is must

5. Designing 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,kerasetc.,.) , LangChain, Flask,FastAPI, prompt engineering.

6. Programming experience in Python

7. Strong written and verbal communications

8. Excellent interpersonal and collaboration skills.

Good-to-Have :

1. Experience working with vectored databases and graph representation of documents.

2. Experience with building or maintaining MLOpspipelines.

3. Experience in Cloud computing infrastructures like AWS Sagemaker or Azure ML for implementing ML solutions is preferred.

4. Exposure to Docker, Kubernetes

SN Role descriptions / 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 models

3. Apply prompt engineer techniques as required by the use case

4. 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 tasks

5. 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.

ref : hirist.tech)