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Building internal AI platform with RAG pipelines and vector search for a global mobile communications provider. Hands-on RAG pipeline and vector database experience required. Exposure to major transactions, dynamic work environment.
Requirements
- 5+ years software engineering experience
- At least 2 years AI/ML systems production experience
- Hands-on RAG pipelines building experience
- Vector databases experience (Pinecone, Weaviate, Elasticsearch, or similar)
- LLM integration into backend services experience
- Strong backend engineering skills
- API design skills
- Microservices skills
- Production operations at scale experience
- Python proficiency
- Practical LLM fine-tuning experience
- Prompt engineering experience
- Evaluation methodologies experience
- CI/CD familiarity
- Containerization (Docker, Kubernetes) familiarity
- Infrastructure-as-code familiarity
- Data security practices familiarity (PII handling, access controls, data classification)
- Experience in regulated environments (financial services, telecommunications, ISO 27001) is an advantage
- Telecommunications domain knowledge is a plus
- Strong communicator
- Ability to explain technical trade-offs to non-technical stakeholders
- Fluent in English
- Self-directed
- Comfortable with ambiguity
- Shaping the function
Tasks
- Design and build internal AI platform
- Develop RAG pipelines and vector search infrastructure
- Create conversational interfaces for internal use
- Evaluate and integrate large language models
- Operate language models in production
- Balance accuracy, latency, and cost
- Build secure ingestion pipelines for internal knowledge
- Index Confluence, SharePoint, and document repositories
- Implement access controls for internal knowledge
- Develop backend services and APIs
- Expose AI capabilities to internal users
- Support product teams building customer-facing features
- Establish engineering standards
- Develop testing, deployment, and monitoring frameworks
- Create model evaluation frameworks
- Manage full development lifecycle
- Focus on shipping and iterating
Work Experience
- 5 years
Education
- Bachelor's degreeOR
- Master's degree
Languages
- English – Fluent
Tools & Technologies
- Python
- RAG
- Pinecone
- Weaviate
- Elasticsearch
- LLM
- API design
- microservices
- CI/CD
- Docker
- Kubernetes
- infrastructure-as-code
- PII handling
- access controls
- data classification
- ISO 27001
Benefits
Career Advancement
- Growth opportunities
Other Benefits
- Major transaction exposure
Informal Culture
- Work with talented team
- International experience
- Open communication culture
Startup Environment
- Dynamic work environment
- Get things done attitude
Learning & Development
- Professional development
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Building internal AI platform with RAG pipelines and vector search for a global mobile communications provider. Hands-on RAG pipeline and vector database experience required. Exposure to major transactions, dynamic work environment.
Requirements
- 5+ years software engineering experience
- At least 2 years AI/ML systems production experience
- Hands-on RAG pipelines building experience
- Vector databases experience (Pinecone, Weaviate, Elasticsearch, or similar)
- LLM integration into backend services experience
- Strong backend engineering skills
- API design skills
- Microservices skills
- Production operations at scale experience
- Python proficiency
- Practical LLM fine-tuning experience
- Prompt engineering experience
- Evaluation methodologies experience
- CI/CD familiarity
- Containerization (Docker, Kubernetes) familiarity
- Infrastructure-as-code familiarity
- Data security practices familiarity (PII handling, access controls, data classification)
- Experience in regulated environments (financial services, telecommunications, ISO 27001) is an advantage
- Telecommunications domain knowledge is a plus
- Strong communicator
- Ability to explain technical trade-offs to non-technical stakeholders
- Fluent in English
- Self-directed
- Comfortable with ambiguity
- Shaping the function
Tasks
- Design and build internal AI platform
- Develop RAG pipelines and vector search infrastructure
- Create conversational interfaces for internal use
- Evaluate and integrate large language models
- Operate language models in production
- Balance accuracy, latency, and cost
- Build secure ingestion pipelines for internal knowledge
- Index Confluence, SharePoint, and document repositories
- Implement access controls for internal knowledge
- Develop backend services and APIs
- Expose AI capabilities to internal users
- Support product teams building customer-facing features
- Establish engineering standards
- Develop testing, deployment, and monitoring frameworks
- Create model evaluation frameworks
- Manage full development lifecycle
- Focus on shipping and iterating
Work Experience
- 5 years
Education
- Bachelor's degreeOR
- Master's degree
Languages
- English – Fluent
Tools & Technologies
- Python
- RAG
- Pinecone
- Weaviate
- Elasticsearch
- LLM
- API design
- microservices
- CI/CD
- Docker
- Kubernetes
- infrastructure-as-code
- PII handling
- access controls
- data classification
- ISO 27001
Benefits
Career Advancement
- Growth opportunities
Other Benefits
- Major transaction exposure
Informal Culture
- Work with talented team
- International experience
- Open communication culture
Startup Environment
- Dynamic work environment
- Get things done attitude
Learning & Development
- Professional development
Like this job?
BetaYour Career Agent finds similar jobs for you every day.
About the Company
1GLOBAL
Industry
Telecommunications
Description
1GLOBAL is a technology-driven global mobile communications provider delivering connectivity solutions to enterprises and consumers, operating as a regulated telecommunications provider across 40 countries.
Not a perfect match?
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