Responsibilities:
1. Strong analytical skill to identify the right AI/ML use case that helps create value.
2. Ownership of the problem statement and creating AI/ML based solve till deployment
3. Collect Data for the identified problem, identify patterns, inferences and hypothesis testing
4. Statistical modelling and validation of hypothesis.
5. Feature Engineering – Remove noise and choose elements of data that can be represented for model training.
6. Labelling and Annotation – Find innovative ways to annotate the data and ensure balancing of representation.
7. Algorithm – Choosing the right approach and algorithm if it’s a regression or classification or machine learning or deep learning or a heuristic one. Stronghold of descriptive, predictive and generative algorithms and when to choose which one for the solve.
8. Accuracy improvement & fine tuning – Ability to fine tune the algorithm by hyper parameters, matrix weights, transfer learning approach.
9. API – Provide prediction as a service for downstream consuming applications (app/edge/vehicle).
10. Maintain the model throughout its lifecycle. Adapting to latest releases and approach for the problem statement, keep updating the training set.
11. Governance – Maintain the standard and limits of the algorithm so it doesn’t breach privacy, ethics and laws of the country in which it is deployed.
12. Cost of operations – Choose an optimal mix of compute, storage and model deployment so that cloud cost is optimal. Optimize operation like caching, avoid repetitive predicts, storage.
13. Mobility domain – Ability to deploy the models on edge devices with embedded support considering the memory and cost trade off.
Skills Required:
1. OS Platform: Linux, Ubuntu – Familiar with shell scripting.
2. Programming language: Python, Java, C++ – Familiar to modify the ML/DL algorithm available open source and build new algorithms using available open source frameworks. Build web based secure API layer for consumption. C++ familiarity for edge device deployment.
3. Tools – Python IDE, Jupyter Notebooks, Jenkins.
4. Deep learning frameworks: Pytorch , tensorflow, Keras, TF Lite, Google AI edge or similar – Ability to understand the frameworks to build the AI/ML model and customize it. Ability to deploy the developed models at edge device.
5. Machine learning frameworks: Numpy, scipy, scikit – Ability to build ML algorithms, matrix multiplications, dealing with higher dimensional matrix and tensors using the available frameworks.
6. NLP: BERT, Word2vec, LSTM, RNN, Named entity recognition – Familiar with Natural Language Processing algorithms to solve text based problems.
7. Computer Vision: CNN, OCR – Familiar with Computer vision algorithms to solve video/image/audio-based problems.
8. Algorithms: Regression, Classification, clustering, outlier detection, Principal component Analysis, Noise reduction, Knowledge graphs, Graph based algorithms, NLP algorithms – Familiar with standard algorithms
9. MLOPS: Ability to deploy and maintain live models in production with frequent updates. Ability to annotate data for creating learning set.
10. Cloud skill: AWS, GCP, Databricks, Azure – Ability to deploy maintain and serve models on cloud for downstream applications.
Qualifications
- Computer Science and Software Development skills
- Minimum 6+ Years experience required
- Back-End Web Development skills
- Programming and Object-Oriented Programming (OOP) skills
- Experience in developing AI/ML algorithms and applications
- Strong problem-solving and analytical skills
- Excellent teamwork and communication abilities
- Bachelor's or Master's degree in Computer Science or related field
Job Types: Full-time, Permanent
Pay: ₹477,103.79 - ₹1,000,000.00 per year
Benefits:
- Food provided
- Health insurance
- Provident Fund
Work Location: In person
