Senior Machine Learning Engineer

4 weeks ago

Bengaluru, India DataWeave Full time
About usDataWeave provides Retailers and Brands with “Competitive Intelligence as a Service” that enables them to take key decisions that impact their revenue. Powered by AI, we provide easily consumable and actionable competitive intelligence by aggregating and analyzing billions of publicly available data points on the Web to help businesses develop data-driven strategies and make smarter decisions.

Data Science @DataWeaveWe the Data Science team at DataWeave (called Semantics internally) build the core machine learning backend and structured domain knowledge needed to deliver insights through our data products. Our underpinnings are: innovation, business awareness, long term thinking, and pushing the envelope. We are a fast paced lab within the org applying the latest research in Computer Vision, Natural Language Processing, and Deep Learning to hard problems in different domains.

How we work?It's hard to tell what we love more, problems or solutionsEveryday, we choose to address some of the hardest data problems that there are. We are in the business of making sense of messy public data on the web. At serious scale Read more on BecomeaDataWeaver

What do we offer?Some of the most challenging research problems in NLP and Computer Vision. Huge text and image datasets that you can play withAbility to see the impact of your work and the value you're adding to our customers almost immediately.Opportunity to work on different problems and explore a wide variety of tools to figure out what really excites you.A culture of openness. Fun work environment. A flat hierarchy. Organization wide visibility. Flexible working hours.Learning opportunities with courses and tech conferences. Mentorship from seniors in the team.Last but not the least, competitive salary packages and fast paced growth opportunities.

Who are we looking for?The ideal candidate is a strong software developer with experience end to end pipelines for production grade data science applications at scale. Such a candidate has keen interest in liaising with the delivery and product teams to understand a solution process, and translate that into an automated workflow. You are also expected to develop capabilities that optimize machine learning operations.We are looking for someone with a Bachelor's degree and 4+ years of experience working on ML Engineering & Operations. Knowledge of Machine Learning fundamentals is also desired.If you have 6+ years of relevant experience, you will be considered for a senior role.

Role and Responsibilities

Understand the business problems we are solving. Take initiatives to build new capabilities. Develop business awareness. Explore productization opportunities.Design system & data architectures for our current workflows.Build end to end pipelines using tools like Python, Airflow, and Kafka.Collaborate with other teams such as Delivery, Products & Platforms to seamlessly integrate solutions.Collaborate with Data Scientists to deploy and monitor Machine Learning models at scale.Constantly think scale, think automation. Measure everything. Optimize proactively.Take ownership of the projects you are working on. Work with minimal supervision.Add passion and vibrance to the team. Push the envelope. Be a mentor to junior members of the team.

Relevant set of skills

Strong in software engineering and code design.Adept in Python programming with a good understanding of data structures.Working knowledge of Azure and AWS platforms.Experience in building production grade systems.Experience in API development.Experience with containerization using Docker & Kubernetes.Hands on experience with various databases.Use the command line like a pro. Be proficient in Git and other essential software development tools.Be a self-starter — someone who thrives in fast paced environments with minimal ‘management’.It's a huge bonus if you have some personal projects (including open source contributions) that you work on during your spare time. Show off some of your projects you have hosted on GitHub.

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