Mavenir is building the future of networks and pioneering advanced technology, focusing on the vision of a single, software-based automated network that runs on any cloud. As the industry's only end-to-end, cloud-native network software provider, Mavenir is transforming the way the world connects, accelerating software network transformation for 250+ Communications Service Providers in over 120 countries, which serve more than 50% of the world’s subscribers.
Role Summary
Plans, designs, develops and tests software systems or applications for software enhancements and new products including cloud-based or internet-related tools.
Key Responsibilities
Generative AI application development
LLMs, SLMs, RAG, agents, tool-calling, and prompt engineering
AI gateway or AI platform concepts
LLM fine-tuning and evaluation
ML model training and ML pipelines
Python-based backend development
FastAPI, REST APIs, microservices, and async processing
Vector databases such as Milvus, FAISS, Qdrant, Weaviate, or Pinecone
ML frameworks such as PyTorch, TensorFlow, Hugging Face Transformers, or Scikit-learn
Fine-tuning frameworks such as PEFT, LoRA, QLoRA, TRL, or Unsloth
Workflow orchestration using Airflow, Prefect, Kubeflow, Dagster, or similar tools
Containerization using Docker
Kubernetes-based deployment experience
Observability using logs, metrics, traces, dashboards, and alerts
Strong understanding of scalable system design
Good software engineering fundamentals, clean coding, testing, and documentation
Job Requirements
• Design, develop, and deploy AI solutions across Generative AI, AI Platforms, Voice AI, LLM Fine-Tuning, and MLOps.
• Build production-grade applications using LLMs, RAG, Agents, and Prompt Engineering.
• Develop scalable APIs, backend services, and AI/ML pipelines.
• Contribute to AI platform development, performance optimization, and production deployments.
Must-Have Skills:
• Strong programming skills in C, C++ and Python.
• Strong understanding of scalable system design.
• Hands-on experience with GenAI, LLMs, RAG, Agents, and Prompt Engineering.
• Experience building APIs, backend systems, and microservices.
• Knowledge of ML training, evaluation, and MLOps workflows.
• Experience with PyTorch, TensorFlow, Hugging Face, or similar frameworks.
• Familiarity with Docker, Kubernetes, Vector Databases, and cloud-native environments.
Preferred Qualifications:
• Bachelor's or master's degree in computer science, AI, ML, Data Science, or a related field.
Accessibility
Mavenir is committed to working with and providing reasonable accommodation to individuals with physical and mental disabilities. If you require any assistance, please state in your application or contact your recruiter.
Mavenir is an Equal Employment Opportunity (EEO) employer and welcomes qualified applicants from around the world, regardless of their ethnicity, gender, religion, nationality, age, disability, or other legally protected status.
Summary
Design and deploy AI solutions across generative AI, AI platforms, LLM fine-tuning, and MLOps; build production-grade LLM, RAG, agent, and prompt-engineering applications; develop scalable APIs, backend services, and AI/ML pipelines. Requires strong C, C++, and Python, scalable system design, GenAI and LLM experience, API and microservices development, and ML training/MLOps knowledge.
Responsibilities
Generative AI application development; LLMs, SLMs, RAG, agents, tool-calling, and prompt engineering; AI gateway or platform concepts; LLM fine-tuning and evaluation; ML model training and pipelines; Python backend development; FastAPI, REST APIs, microservices, and async processing; vector databases; ML frameworks; fine-tuning frameworks; workflow orchestration; containerization; Kubernetes deployment; observability; scalable system design; software engineering fundamentals, clean coding, testing, and documentation; design, develop, and deploy AI solutions; build production-grade applications; develop scalable APIs, backend services, and AI/ML pipelines; contribute to AI platform development, optimization, and production deployments.
Qualifications
Must-have skills: strong programming in C, C++, and Python; scalable system design; hands-on GenAI, LLMs, RAG, agents, and prompt engineering; API, backend, and microservices experience; ML training, evaluation, and MLOps; PyTorch, TensorFlow, or Hugging Face; Docker, Kubernetes, vector databases, and cloud-native environments. Preferred: bachelor's or master's degree in computer science, AI, ML, data science, or related field.
Education requirements
Bachelor's or master's degree in computer science, AI, ML, Data Science, or a related field (preferred).