AI Engineer

About the Role

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.