Software Architect (8-16 yrs)

About the Team :

MLP Team is responsible to ensure that at organization , Data Scientists are able to train and host classic ML models, Large Language Models (LLM) and Large Image Models (LIM) at scale. This is a vastly exciting and rapidly evolving domain within Data Science.

As of July-2024, MLP Team has enabled Data Scientist to go Live with multiple use cases using in-house open source fine tuned models. Azure OpenAI ChatGPT 3.5, GPT 4 and GPT 4.o and Google Gemini 1.5 Pro and Flash models.

You are responsible for :

1. Architects at organization are responsible for driving Technology & good practices in Engineering in their respective teams.

2. Actively participate in development along with team members for as much as 75% of their time, creating modules & systems that can then be treated as a working reflection of the best practices.

3. Being responsible for Scaling, Performance & Quality for the team.

4. Setting up best practices to help the team achieve the above and constantly thinking about improving the technology use are your responsibilities.

5. Experiment with new & relevant technologies and tools, and drive adoption while measuring yourself on the impact you can create.

6. Implementation of long-term technology vision for your team.

7. Responsible for the architecture of your platform; ensuring it is aligned to the long term requirements and the platform vision.

8. Active participant in technology forums and represent company in internal and external forums.

9. Creating architectures & designs for new solutions around existing and new problem spaces at organization.

10. Drive technology & tool choices for your team & be responsible for them.

11. Going beyond your role and contributing to make the organization & business better is an expectation.

You should have :

1. You serve as a technical expert, providing direction and oversight on software development methodologies, technologies, and best practices.

2. You are proficient in programming on either Python or Java.

3. You have experience developing REST APIs, SDKs using Python or Java.

4. You are proficient at developing containerized applications leveraging Docker, Kubernetes or related container stack.

5. You are a champion of SDLC practices.

6. You understand concepts of application development, infrastructure management, data engineering and platform constructs.

7. You are aware of the Machine Learning/Deep Learning development life cycle covering aspects of EDA, Model development, (re) training and inference pipelines.

8. You have experience with Deep Learning tools and libraries such as TensorFlow, Keras, and PyTorch.

9. You have exposure to Data Preparation needs for different use cases. Data preparation involves tasks such as data cleaning, data transformation, feature engineering, and data integration.

10. You have knowledge of storage systems and different Database systems.

11. You are a hustler, tinkerer with open source frameworks.

Educational Qualifications :

1. Bachelors in Computer Science or related field.

2. Must have taken up courses on Machine Learning/Deep Learning/ MLOps or possess a Masters in Data Science or related field.

3. Demonstrable industry or academic experience with Machine Learning/Deep Learning domain. A GitHub repository showcasing portfolio of work on ML/DL Engineering.

Bonus Points if you have :

1. Experience with distributed data processing such as MR, Apache Spark, Apache Beam

2. Understanding of common ML algorithms, Deep Learning, familiarity with NLP or computer vision domain , usage of GPUs in ML lifecycle.

3. Exposure to CUDA programming.

4. Experience with Google Cloud Platform or any other public cloud.

5. Working knowledge of Kubernetes or any container orchestration technology/framework.

What will you do ?

1. As a Machine Learning leader, you will play a crucial role in the development and implementation of cutting-edge artificial intelligence products.

2. Lead a team of technical professionals, providing guidance, mentorship, and support to ensure successful project delivery.

3. Your responsibilities will involve designing and constructing platform tools for deploying sophisticated machine learning models, as well as refining and updating existing systems.

4. Build platform solutions to drive greater productivity for Data Scientists at organization.

5. Build tools to support adoption and quick productization of Data Science solutions.

6. Build on recent advancements in the area of Generative AI, LLMs.

7. Build frameworks to support distributed training, large model training within company on Hybrid compute.

8. Develop services on top of Computer Vision and Natural Language capabilities developed by Data Scientists.

9. Develop self serve platform to allow Data Scientists to manage training, retraining, deployment, scheduling, monitoring, and improvement of ML models.

10. Work on problems such as Data Version Controller, Experiment Tracking, Data Labeling, Active Learning, training large language models, building a cluster of GPUs optimized for inferring and training needs, both.

11. Explore and conduct PoC using open source frameworks , AZURE and GCP offerings on Data Science.