ML Engineer

About Us:
Simplismart enables businesses to build scalable production-grade AI systems and manage the development lifecycle without writing a single line of code. This helps them ship deep learning models in days instead of months, saving them hundreds of thousands of dollars in engineering costs. Our platform allows both amateurs and experts to train and monitor ML models collaboratively on almost any kind of data or use case. Simplismart is headquartered in Bengaluru and has 11-50 employees. Learn more at https://simplismart.ai.

Job Overview:
We are looking for a skilled Machine Learning Engineer to join our team in Bangalore. This is a full-time, mid-level position that requires a minimum of 4 years to a maximum of 6 years of work experience. The ideal candidate will have expertise in machine learning and Python and will be responsible for developing and deploying machine learning models on our platform.

Qualifications And Skills:

1. Expertise in machine learning (Mandatory skill) with a proven track record in developing, deploying, and monitoring ML models.
2. Proficiency in Python (Mandatory skill) with experience in libraries such as TensorFlow, PyTorch, and Scikit-learn.
3. Strong understanding of data science, including data preprocessing, feature engineering, and model evaluation techniques.
4. Proven experience in deep learning, especially with neural network architectures and training methodologies.
5. Solid grasp of statistical methods and their applications in machine learning and data analysis.
6. Experience working with large language models (LLMs) and natural language processing (NLP) frameworks.
7. Ability to work collaboratively with cross-functional teams, including data scientists, product managers, and software engineers.
8. Excellent problem-solving skills, attention to detail, and the ability to troubleshoot technical issues effectively.

Roles And Responsibilities:

1. Develop, implement, and optimize machine learning models tailored to specific business needs and data sets.
2. Collaborate with data scientists and other engineering teams to ensure seamless integration and deployment of ML models.
3. Monitor and maintain the performance of deployed models, making necessary adjustments to improve accuracy and efficiency.
4. Contribute to the continuous improvement of our no-code AI platform by providing insights and feedback based on real-world model deployments.
5. Stay updated with the latest advancements in machine learning and deep learning technologies to incorporate relevant innovations into our platform.
6. Ensure the scalability and robustness of the machine learning solutions by adhering to best practices and industry standards.
7. Provide technical mentorship and guidance to junior team members, fostering a collaborative and inclusive work environment.
8. Document processes, model parameters, and performance metrics to ensure transparency and reproducibility of machine learning experiments.

Desired Skills and Experience:
Machine Learning, Python, Data Science, Deep Learning, Statistics, LLMs

About us
Fastest inference for generative AI workloads. Simplified orchestration via a declarative language similar to terraform. Deploy any open-source model and take advantage of Simplismart’s optimised serving. With a growing quantum of workloads, one size does not fit all; use our building blocks to personalise an inference engine for your needs.

API vs In-house

Renting AI via third-party APIs has apparent downsides: data security, rate limits, unreliable performance, and inflated cost. Every company has different inferencing needs: One size does not fit all. Businesses need control to manage their cost <> performance tradeoffs. Hence, the movement towards open-source usage: businesses prefer small niche models trained on relevant datasets over large generalist models that do not justify ROI.

Need for MLOps platform

Deploying large models comes with its hurdles: access to compute, model optimisation, scaling infrastructure, CI/CD pipelines, and cost efficiency, all requiring highly skilled machine learning engineers. We need a tool to support this advent towards generative AI, as we had tools to transition to cloud and mobile. MLOps platforms simplify orchestration workflows for in-house deployment cycles. Two off-the-shelf solutions readily available:

1. Orchestration platforms with model serving layer: do not offer optimised performance for all models, limiting user’s ability to squeeze performance
2. GenAI Cloud Platforms: GPU brokers offering no control over cost

Enterprises need control. Simplismart’s MLOps platform provides them with building blocks to prepare for the necessary inference. The fastest inference engine allows businesses to unlock and run each model at performant speed. The inference engine has been optimised at three levels: the model-serving layer, infrastructure layer, and a model-GPU-chip interaction layer, while also enhanced with a known model compilation technique.