Senior Lead AI/ML Engineer - R01570774
Brillio (company site, opens in a new tab)6–8 yrsBengaluruOn-siteDirect career pageEst. 20–32 LPA
Posted 3h ago
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Reach people at Brillio
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Brillio you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Design, develop, and maintain scalable machine learning pipelines using Java, Python, or Scala and distributed computing frameworks like Spark, DASK, or Ray.
- Lead end-to-end delivery of low-latency inferencing solutions and build deep learning pipelines for forecasting and personalization with PyTorch and TensorFlow.
- Collaborate with agile teams to implement RESTful services, use real-time streaming tools like Kafka or Storm, and ensure ML workflow quality using containerization and orchestration tools.
Minimum Requirements
- 6 to 8 years of software development experience with at least 5 years building and optimizing ML pipelines and 4 years with distributed computing frameworks (Spark, DASK, Ray).
- Proficiency in Java, Python, or Scala, machine learning frameworks (TensorFlow, PyTorch, Sci-Kit Learn), and statistical analysis including hypothesis testing and regression.
- Experience with real-time streaming technologies (Kafka or Storm), RESTful web services, containerization tools (Docker or PCF), and ML orchestration tools (KubeFlow, BentoML).
- Work onsite at Manyata Tech Park, Bangalore, minimum 4 days per week.
Ideal Candidate Profile
- Experienced in designing and optimizing scalable ML solutions in production environments requiring low-latency inferencing.
- Strong background in distributed computing and advanced statistical modeling applied to machine learning pipelines.
- Skilled at working within cross-functional agile teams integrating ML models into service architectures and utilizing containerized, orchestrated workflows.