Dear Candidate,
About us - Coders Brain is a global leader in its services, digital and business solutions that partners with its clients to simplify, strengthen and transform their businesses. We ensure the highest levels of certainty and satisfaction through a deep-set commitment to our clients, comprehensive industry expertise and a global network of innovation and delivery centers. We achieved our success because of how successfully we integrate with our clients.
Quick Implementation - We offer quick implementation for the new onboarding client.
Experienced Team - We’ve built an elite and diverse team that brings its unique blend of talent, expertise, and experience to make you more successful, ensuring our services are uniquely customized to your specific needs.
One Stop Solution - Coders Brain provides end-to-end solutions for the businesses at an affordable price with uninterrupted and effortless services.
Ease of Use - All of our products are user friendly and scalable across multiple platforms. Our dedicated team at Coders Brain implements keeping the interest of enterprise and users in mind.
Secure - We understand and treat your security with utmost importance. Hence we blend security and scalability in our implementation considering long term impact on business benefit.
Position: Machine Learning
Experience: 5+ years
Education: BE/B.Tech
Responsibilities:
Mandatory Skill: Background or experience in building and scaling Generative AI Applications, specifically
around frameworks like Langchain, PGVector, Pinecone, AzureML
Industry experience with popular ML frameworks such as Spark MLlib, Keras, Tensorflow, PyTorch,
HuggingFace Transformers and libraries (like scikit-learn, spacy, gensim, CoreNLP etc.).
Experience in designing scalable services controller architecture using FastAPI.
Collaborate with colleagues across multiple teams (Data Science and Data Engineering) on unique
machine learning system challenges at scale.
Leverage distributed training systems to build scalable machine learning pipelines for model training and
deployments in IT/OT Products space.
Design and implement solutions to optimize distributed training execution in terms of model
hyperparameter optimization, model training/inference latency and system-level bottlenecks.
Research and impalement state of the art LLM models for different business use cases including finetuning
and serving the LLMs.
Ensure ML Model performance, uptime, and scale, maintaining high standards of code quality and
thoughtful design quality and monitoring.
Optimize integration between popular machine learning libraries and cloud ML and data processing
frameworks.
Build Deep Learning models and algorithms with optimal parallelism and performance on CPUs/ GPUs.
Your background and who you are:
MS or Ph.D. in Computer Science, Software Engineering, Electrical Engineering, or related fields.
3+ years of industry experience with Python in a programming intensive role.
2+ years of experience with one or more of the following machine learning topics: classification, clustering,
optimization, recommendation system, graph mining, deep learning.
3+ years of industry experience with distributed computing frameworks such as Spark, Kubernetes
ecosystem, etc.
3+ years of industry experience with popular ml frameworks such as Spark MLlib, Keras, Tensorflow,
PyTorch, HuggingFace Transformers and libraries (like scikit-learn, spacy, gensim, CoreNLP etc.).
3+ years of industry experience with major cloud computing services.
Background or experience in building and scaling Generative AI Applications, specifically around frameworks
like Langchain, PGVector, Pinecone, AzureML.
Prior experience in building data products and established a track record of innovation would be a big plus.
An effective communicator – you shall be an ambassador of Honeywell’s Machine Learning engineering
at external forums and have the ability to explain technical concepts to a non- technical audience.
Preferred Qualifications:
Proficient Python/PySpark coding experience
Proficient in containerization services
Proficient in Azure ML to deploy the models
Experience with working in CICD framework
Motivation to make downstream modelers’ work smoother
