Machine Learning Engineer - Deep Learning (5-8 yrs)

About the Role :

We are seeking a highly motivated and talented Machine Learning Engineer to join our growing team.

In this role, you will be responsible for developing and deploying cutting-edge machine learning solutions across a variety of domains.

You will work closely with cross-functional teams to understand business needs, design and implement innovative ML models, and ensure the successful delivery of high-quality products.

Responsibilities :

- Design, develop, and deploy machine learning models for various applications, including but not limited to:

Natural Language Processing (NLP) :

- Text classification, sentiment analysis, topic modeling, machine translation, question answering, chatbots.

Computer Vision : Image classification, object detection, image segmentation, video analysis.

Speech Recognition : Speech-to-text, speaker recognition, noise suppression.

- Conduct research and experimentation with the latest advancements in machine learning, including deep learning, reinforcement learning, and generative AI.

- Collaborate with data scientists, engineers, and product managers to define project scope, gather requirements, and translate business needs into technical solutions.

- Develop and maintain robust and scalable machine learning pipelines using cloud-based platforms (e.g , GCP, AWS).

- Perform model evaluation, tuning, and optimization to achieve high accuracy and performance.

- Stay abreast of the latest research and industry trends in machine learning.

- Contribute to the development of best practices and methodologies for machine learning development and deployment.

Qualifications :

Required :

- Strong foundation in machine learning and deep learning :

- Proficiency with frameworks such as TensorFlow, PyTorch, scikit-learn, and Keras.

- Expertise in Natural Language Processing (NLP): Experience with tasks such as part-of-speech tagging, named entity recognition (NER), text parsing, and Optical Character Recognition (OCR).

- Unstructured Data Handling: Experience working with text, image, and audio data.

- Large Language Models (LLMs): Understanding of LLMs (e.g , GPT, BERT, T5), including prompt engineering, fine-tuning, and transformer-based architectures.

Prototyping & Innovation :

- Ability to quickly prototype ML solutions to validate use cases and drive innovation.

Cloud Platforms :

- Familiarity with public cloud platforms like GCP or AWS for deploying and scaling ML solutions.

Collaboration Tools :

- Proficiency with Jupyter Notebook and VS Code.

- Excellent communication and interpersonal skills

Good to Have :

Audio Processing Skills :

- Hands-on experience in audio signal processing, feature extraction, and analysis using libraries like LibROSA, PyDub, and SpeechRecognition.

NLP & Audio Libraries :

- Familiarity with key libraries such as Hugging Face Transformers, NLTK, SpaCy, and SpeechBrain.

MLOps Principles :

- Understanding of MLOps principles, including model versioning, monitoring, and automated pipelines