Machine Learning Engineer- 2

As an ML Engineer in the team, you will work closely with Data Science, Engineering, Platform, Product and Operations teams to build state-of- the-art ML based solutions for B2B SaaS products. This will entail applying advanced ML algorithms at scale for core products and developing robust end to end production pipelines which includes Human-in-the-Loop component to boost the quality.

The ideal candidate will have a strong background in machine learning model development, deploying large scale, high throughput machine learning pipelines to production, experience with developing and managing frameworks for machine learning platforms, which they can utilise to manage and improve our company's AI/ML initiatives.

• Contribute to development of multiple AI driven end to end pipelines that allows for deployment and scalability of machine learning models
• Build an end-to-end machine learning platform, covering all lifecycle stages of a model, to ease model development and deployment
• Build tools and capabilities that help with data ingestion to feature engineering, data management and organisation
• Deploy cutting edge algorithms like LLMs etc. on GPUs along with distributed computing for scalability
• Contribute to tools and capabilities for model management and model performance monitoring
• Implement the best engineering practices for scaling ML-powered features, with a goal to enable the fast iteration of and efficient experimentation with novel features
• Champion and own the ML infrastructure roadmap, in collaboration with Data Science and other platform teams

What we are looking for -

• Bachelor's or Masters in Computer Science or Maths/Stats from a reputed college with 4+ years of experience in solving of experience in machine learning engineering problems
• Prior experience with deploying large scale machine learning models to production, both in batch and real-time setups
• Experience with distributed computing frameworks like Spark / Map-Reduce etc.
• Cloud experience with any one provider ( AWS / GCP / Azure )
• Experience with Infra-as-code tools like Terraform
• Experience and understanding of the entire machine learning pipeline from data ingestion to production
• Experience with machine learning operations, software engineering, and architecture
• Experience architecting and building an AI pipelines that supports productionization of ML models
• Strong programming skills in a scientific computing language such as Python, SQL
• Experience using frameworks for machine learning and data science like scikit-learn, pandas, NumPy
• Experience with Databricks is a plus
• Experience working with ML tools such as Tensorflow, Keras, and Pytorch
• Ability to take successful, complex research ideas from experimentation to production
• Excellent written and oral communication skills and the capability to drive cross-functional requirements with product and engineering teams
• Good depth and breadth in machine learning (theory and practice), optimization methods, data mining, statistics and linear algebra

Why you will love to work with us -

We have multiple brands, which are among the biggest brands operating on Amazon, as our customers. You will be responsible for working with their data and developing models and algorithms to drive lift in tune of hundreds of thousands of dollars.

• Opportunity to work on cutting edge technologies in text and images data like LLMs, OCR, transformer based embeddings models.
• Deploy code using the most advanced tech stack like AWS, GCP etc.
• You will own projects end to end i.e., from exploration to final production
• You will be empowered to make decisions. It implies more responsibilities but high learning
• Immense opportunities to learn and develop ML based SaaS products for brands and get company-wide recognition
• Forget endless meetings. Get things done and start making an impact from day 1
• Work hard, have amazing fun, create history, build the future of eCommerce
• Start-up environment with a strong culture and values to accelerate your career