PythonREQUIREMENT:
- Previous experience of working in large scale data engineering
- 4+ years of experience working in data engineering and/or backend technologies with cloud experience (any) is mandatory.
- Previous experience of architecting and designing backend for large scale data processing.
- Familiarity and experience of working in different technologies related to data engineering – different database technologies, Hadoop, spark, storm, hive etc.
- Hands-on and have the ability to contribute a key portion of data engineering backend.
- Self-inspired and motivated to drive for exceptional results.
- Familiarity and experience working with different stages of data engineering – data acquisition, data refining, large scale data processing, efficient data storage for business analysis.
- Familiarity and experience working with different DB technologies and how to scale them.
RESPONSIBILITY:
- End to end responsibility to come up with data engineering architecture, design, development and then implementation of it.
- Build data engineering workflow for large scale data processing.
- Discover opportunities in data acquisition.
- Bring industry best practices for data engineering workflow.
- Develop data set processes for data modelling, mining and production.
- Take additional tech responsibilities for driving an initiative to completion
- Recommend ways to improve data reliability, efficiency and quality
- Goes out of their way to reduce complexity.
- Humble and outgoing - engineering cheerleaders.
Shubham Vishwakarma
Full Stack Developer - Averlon
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Location: Bangalore / Bengaluru, Hyderabad, Pune, Mumbai, Mohali, Panchkula, Delhi
Experience: 6–10+ Years
Night Shift - 9 pm to 6 am
About the Role:
We’re looking for a Senior Cloud & ML Infrastructure Engineer to lead the design,scaling, and optimization of cloud-native machine learning infrastructure. This role is ideal forsomeone passionate about solving complex platform engineering challenges across AWS, witha focus on model orchestration, deployment automation, and production-grade reliability. You’llarchitect ML systems at scale, provide guidance on infrastructure best practices, and work cross-functionally to bridge DevOps, ML, and backend teams.
Key Responsibilities:
● Architect and manage end-to-end ML infrastructure using SageMaker, AWS StepFunctions, Lambda, and ECR
● Design and implement multi-region, highly-available AWS solutions for real-timeinference and batch processing
● Create and manage IaC blueprints for reproducible infrastructure using AWS CDK
● Establish CI/CD practices for ML model packaging, validation, and drift monitoring
● Oversee infrastructure security, including IAM policies, encryption at rest/in-transit, andcompliance standards
● Monitor and optimize compute/storage cost, ensuring efficient resource usage at scale
● Collaborate on data lake and analytics integration
● Serve as a technical mentor and guide AWS adoption patterns across engineeringteams
Required Skills:
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● Proven experience building and maintaining ML pipelines with services like SageMaker,ECS/EKS, or custom Docker pipelines
● Strong knowledge of networking, IAM, VPCs, and security best practices in AWS
● Deep experience with automation frameworks, IaC tools, and CI/CD strategies
● Advanced scripting proficiency in Python, Go, or Bash
● Familiarity with observability stacks (CloudWatch, Prometheus, Grafana)
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● Experience designing systems for physical robot fleet telemetry, diagnostics, and control
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● Involvement with real-time streaming, MQTT, or edge inference workflows
● Hands-on experience with ROS 2 (Robot Operating System) or similar robotics frameworks, including launch file management, sensor data pipelines, and deployment to embedded Linux devices
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Full Stack Developer - Averlon