Principal Machine Learning Engineer - LLM (5-10 yrs)

We are seeking a highly skilled and experienced Principal Machine Learning Developer to join our dynamic team. The ideal candidate will have a strong background in machine learning (ML), deep learning (DL), and large language models (LLMs). They should be proficient in Python and possess expertise in conventional ML algorithms, DL techniques, LLM,NL,Generative AI and prompt engineering.

Responsibilities :

Machine Learning Development :

- Design, develop, and implement ML models and algorithms to solve complex problems.

- Work on various ML projects involving data preprocessing, feature engineering, model selection, and evaluation.

- Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions.

Deep Learning :

- Develop and deploy DL models using frameworks like TensorFlow, PyTorch, or Keras.

- Optimize DL models for performance and scalability.

- Stay updated with the latest advancements in DL and apply them to improve existing models.

Large Language Models :

- Develop and fine-tune large language models for various NLP tasks.

- Implement prompt engineering techniques to enhance model performance and accuracy.

- Experiment with state-of-the-art LLM architectures and methodologies.

Software Development :

- Write clean, maintainable, and efficient code in Python.

- Conduct code reviews and ensure adherence to best practices and coding standards.

- Implement version control using Git and collaborate with the development team through GitHub or similar platforms.

Data Handling :

- Work with large datasets and perform data preprocessing, cleaning, and augmentation.

- Implement data pipelines and ensure data integrity and quality throughout the ML lifecycle.

Research and Innovation :

- Stay abreast of the latest research and developments in the field of ML, DL, and LLMs.

- Propose and explore innovative solutions to improve model performance and address new challenges.

Mentorship :

- Mentor and guide junior ML engineers and team members.

- Share knowledge and best practices within the team to foster a collaborative and learning environment.

Areas of Expertise :

1. NLP Model Development : Develop, implement, and fine-tune NLP models for a variety of applications, including text classification, semantic similarity searches, named entity recognition, and chatbots.

2. Machine Learning : Design, train, and evaluate machine learning models to enhance NLP solutions, including deep learning approaches such as transformers.

3. Data Preprocessing : Handle data preprocessing tasks, such as text cleaning, tokenization, and feature engineering, to optimize model performance.

4. Feature Engineering : Extract and engineer meaningful features from text data to improve model accuracy and efficiency.

5. Evaluation and Optimization : Continuously assess model performance, fine-tune hyperparameters, and optimize models for real-world deployment.

6. ML Pipeline : ML model deployment lifecycle tools like (MLflow)

7. LLMs : usage of Large Language Model to simplify various tasks

8. Research and Innovation : Stay updated with the latest NLP and machine learning research, and actively contribute to the development of state-of-the-art models and techniques.

9. Collaboration : Collaborate with cross-functional teams, including data scientists, software engineers, and product managers, to ensure seamless integration of NLP solutions into our products.

10. Documentation : Maintain thorough documentation of code, models, and processes for knowledge sharing and team collaboration.

Qualifications :

- Bachelor's or higher degree in computer science, machine learning, artificial intelligence, or a related field.

- Proven experience in developing NLP applications and machine learning models, with at least [5 years] years of relevant work experience.

- Strong proficiency in programming languages such as Python and knowledge of NLP libraries and frameworks like NLTK, spaCy's, Transformers, or Hugging Face.

- Hands-on experience with deep learning frameworks such as TensorFlow or PyTorch.

- Should have experience on working with LLM

- Should have worked on RAG pipeline

- Strong understanding of NLP techniques, including word embeddings, attention mechanisms, and sequence-to-sequence models.

- Excellent problem-solving skills and a data-driven approach to decision-making.

- Strong communication skills and the ability to work in a collaborative team environment.

- A track record of publishing research papers, contributing to open-source NLP projects, or participating in NLP competitions is a plus.