AIML Engineer

We are looking for AIML Engineer:

Role: AI/ML Engineer

Experience Range: (3 -5 Yrs. ) | (6-8 Yrs. )

Skills:

• Very good Skills in Python, Pandas, NumPy, SQL Excellent understanding of Machine Learning algorithms, Gen AI frameworks and models.
• Should have at least one implementation experience in Gen AI: Prompt Engineering, GANs, OpenAI, Dall-E, Stable Diffusion, Langchain, Lllama
• Very good exposure to NLP and Computer Vision models, frameworks, techniques, concepts and tools: TensorFlow, PyTorch, XGBoost, spaCy, BERT, NLTK, Stanford NLP, scikit-learn, OpenCV

Role Description:

1. Play a role of lead/consultant for end-to-end AI/ML solutions including data, technology infrastructure and models.

2. Good understanding of AI-ML machine learning life cycle management, concepts on ML algorithm, ML techniques, and operational experience.

3. Design and development of models according to the business objective, along with metrics to track their progress.

4. Optimize the models in terms of trainable parameters and operations to meet the target platform.

5. Extend existing ML libraries and frameworks to support missing layers or other features.

6. Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world

7. Verifying data quality, and/or ensuring it via data cleaning

8. Supervising the data acquisition process if more data is needed

9. Defining the pre-processing or feature engineering to be done on a given dataset

10. Defining data augmentation pipelines

11. Training models and tuning their hyper parameters

12. Analysing the errors of the model and designing strategies to overcome them

13. Deploying models with experiment tracking.

14. Work with the technology team to plan, execute, and deliver AI/ML Product based projects. Contribute towards the best practices regarding handling the projects.

15. Provide technical design, implementation, and support services around the creation of APIs, Models and Integration Pipelines.

16. Knowledge of data warehousing concepts, including data warehouse technical architectures, infrastructure components, ETL/ ELT and reporting/analytic tools

17. Environments Understanding of the auxiliary practical concerns in production ML systems.

18. Strong communication, presentation and consulting skills, including technical writing skills.