Forward Deployed AI Engineer - Python/RAG

About the Role :

As a Forward Deployed AI Engineer, you will work directly with Telecom Service Providers (CSPs), OEMs, and internal product teams to design, build, and deploy production-grade AI solutions for network observability and AIOps.

This is a customer-facing, builder-first role where you'll translate complex network operations challenges into AI-powered applications using LLMs, Retrieval-Augmented Generation (RAG), AI agents, and model fine-tuning techniques.

You will own the complete lifecyclefrom gathering customer requirements and building proof of concepts to deploying scalable production solutionswhile influencing the company's product roadmap through real-world customer insights.

Key Responsibilities :

• Partner with Telecom CSPs and OEM customers to identify AI use cases across network observability, AIOps, and intelligent automation.

• Design and develop LLM-powered observability solutions for :

• Log anomaly detection

• Intelligent alerting

• Root Cause Analysis (RCA)

• Predictive fault management

• Build RAG-based and Agentic AI applications using LangChain and DSPy for :

• Network knowledge retrieval

• Runbook automation

• NOC operational assistance

• Fine-tune foundation models using telecom datasets including :

• Syslogs

• SNMP traps

• NetFlow

• CDR/EDR

• Signaling logs (S1AP, Diameter, GTP)

• RAN/Core KPI telemetry

• Integrate AI applications with observability platforms such as OpenTelemetry, Prometheus, Grafana, Kafka, Elasticsearch, and OSS/NMS systems.

• Conduct customer workshops, PoC demonstrations, technical evaluations, and model performance reviews.

• Collaborate with Product and Research teams to convert customer feedback into platform enhancements.

• Build reusable accelerators, deployment templates, and technical documentation for future implementations.

Required Skills :

• AI & LLM Engineering : Strong Python programming, LangChain, DSPy, Retrieval-Augmented Generation (RAG), AI Agents, Prompt Engineering, LLM Evaluation & Benchmarking, Model Fine-tuning (LoRA, QLoRA, SFT, RLHF).

• Machine Learning & AI Platforms : Open-source LLMs (Llama, Mistral, Phi, Qwen), Vector Databases (Pinecone, Weaviate, Chroma).