REMOTE · FULLTIME
Machine Learning Engineer
Ratna Global Technologies
Remote · onsite · Posted 70d ago
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Undisclosed6–11 yrsfulltimeRemote
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Section · 01
About this role
Job Details:
Role: ML Engineer
Experience: 6+ Years
Notice Period: Immediate to 15 Days.
Qualification: Graduation
Role Summary
To summarize, we are looking for contractors who can contribute directly to our ML Platform team's core systems—training pipelines, feature store, model deployment, and observability. Strong candidates should have real production experience with MLOps / ML infra, Sagemaker, AWS, Kubernetes, Terraform, Python or Go, and ideally hands-on experience with feature stores, point-in-time correct data pipelines, and real-time or batch inference systems.
Key Responsibilities
• Experience: 5–8+ years in backend, data infrastructure, or ML infrastructure, with clear evidence of shipping production systems rather than only experimental work.
• Strong ML platform / MLOps background: Hands-on experience building or operating model training, batch inference, real-time inference, release, and monitoring systems in production.
• Feature store depth: Experience with online/offline feature stores, point-in-time correctness, training-serving consistency, low-latency feature retrieval, and feature lifecycle/governance. Experience with Chalk or an equivalent framework is highly relevant.
• Cloud + infra fluency: Strong AWS-heavy experience, specifically with SageMaker, S3, Lambda, Kinesis, DynamoDB, Kubernetes, Docker, and Terraform.
• Programming strength: High proficiency in Python is required, and Go is a strong plus for this team's stack.
• Data platform fundamentals: Familiarity with Snowflake, Airflow, streaming systems, schema/data quality, and scalable ETL frameworks, as the MLP work sits closely with data-compute concerns.
• Reliability / observability mindset: Familiarity with SLOs, dependency graphs, drift detection, explainability, cohorting, and operational design.
• Cross-functional execution: Comfort partnering directly with Data Science, Risk, Product, Analytics, and Data Engineering teams.
Strong Signals (Good Candidates)
o Built scalable distributed systems
o Worked on ML pipelines / feature stores
o Hands-on with dbt / data modelling
o Experience with Terraform / Infra as Code
o Designed data platform architecture end-to-end
o Implemented data governance & compliance frameworks
o Experience with Data Mesh / domain-driven design
o Led teams / mentored engineers
o Built self-service data platforms
Role: ML Engineer
Experience: 6+ Years
Notice Period: Immediate to 15 Days.
Qualification: Graduation
Role Summary
To summarize, we are looking for contractors who can contribute directly to our ML Platform team's core systems—training pipelines, feature store, model deployment, and observability. Strong candidates should have real production experience with MLOps / ML infra, Sagemaker, AWS, Kubernetes, Terraform, Python or Go, and ideally hands-on experience with feature stores, point-in-time correct data pipelines, and real-time or batch inference systems.
Key Responsibilities
• Experience: 5–8+ years in backend, data infrastructure, or ML infrastructure, with clear evidence of shipping production systems rather than only experimental work.
• Strong ML platform / MLOps background: Hands-on experience building or operating model training, batch inference, real-time inference, release, and monitoring systems in production.
• Feature store depth: Experience with online/offline feature stores, point-in-time correctness, training-serving consistency, low-latency feature retrieval, and feature lifecycle/governance. Experience with Chalk or an equivalent framework is highly relevant.
• Cloud + infra fluency: Strong AWS-heavy experience, specifically with SageMaker, S3, Lambda, Kinesis, DynamoDB, Kubernetes, Docker, and Terraform.
• Programming strength: High proficiency in Python is required, and Go is a strong plus for this team's stack.
• Data platform fundamentals: Familiarity with Snowflake, Airflow, streaming systems, schema/data quality, and scalable ETL frameworks, as the MLP work sits closely with data-compute concerns.
• Reliability / observability mindset: Familiarity with SLOs, dependency graphs, drift detection, explainability, cohorting, and operational design.
• Cross-functional execution: Comfort partnering directly with Data Science, Risk, Product, Analytics, and Data Engineering teams.
Strong Signals (Good Candidates)
o Built scalable distributed systems
o Worked on ML pipelines / feature stores
o Hands-on with dbt / data modelling
o Experience with Terraform / Infra as Code
o Designed data platform architecture end-to-end
o Implemented data governance & compliance frameworks
o Experience with Data Mesh / domain-driven design
o Led teams / mentored engineers
o Built self-service data platforms
Sourced from naukri
Section · 02
Skills
PythonSQLMachine Learning
Section · Company
About Ratna Global Technologies
Ratna Global Technologies
IT Services & Consulting
350
employees
2017
9 years old
Hyderabad / Secunderabad, Telangana
India
₹7.6L PA avg
Avg at Ratna Global Technologies
About
An IT consulting firm offering comprehensive technological solutions to enhance business efficiency and service delivery globally.
Industries
IT Services & Consulting
Employee ratings
10 reviews
3.8/ 5
Culture
3.3
Career growth
3.1
Work-life
3.5
Employees rate it well for
Compensation & Benefits38Work-Life Balance38
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