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 :

1. Log anomaly detection

2. Intelligent alerting

3. Root Cause Analysis (RCA)

4. Predictive fault management

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

1. Network knowledge retrieval

2. Runbook automation

3. NOC operational assistance

- Fine-tune foundation models using telecom datasets including :

1. Syslogs

2. SNMP traps

3. NetFlow

4. CDR/EDR

5. Signaling logs (S1AP, Diameter, GTP)

6. 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).

- Observability & Data Engineering : OpenTelemetry, Prometheus, Grafana, ELK Stack / Loki / OpenSearch, Kafka / Flink, Log Analytics, Streaming Data Pipelines.

- Cloud : AWS / GCP / Azure.

Required Qualifications :

- 23 years of hands-on experience in Software Engineering or Machine Learning Engineering.

- Experience building and deploying production-grade LLM applications such as RAG systems, AI agents, or fine-tuned models.

- Strong Python programming skills with experience writing clean, maintainable, and well-tested code.

- Hands-on experience with at least one LLM orchestration framework : LangChain, DSPy, LlamaIndex, or similar.

- Experience fine-tuning open-source foundation models.

- Ability to work directly with enterprise or telecom customers, gather requirements, and communicate technical solutions effectively.

- Excellent verbal and written communication skills.

- Based in India with willingness to travel up to 30% for customer engagements.

Preferred Qualifications :

- Experience working with Telecom observability data including : 4G/5G KPIs, RAN/Core telemetry, CDR/EDR, Signaling protocols (Diameter, GTP, S1AP).

- Experience working with telecom OEMs such as Ericsson, Nokia, Huawei, Cisco, or Juniper.

- Experience building AIOps, anomaly detection, or Root Cause Analysis (RCA) platforms for NOC/SOC environments.

- Knowledge of networking and telemetry standards including : SNMP, NETCONF/YANG, OpenConfig, gRPC Streaming Telemetry, sFlow / NetFlow.

- Experience with MLOps platforms such as MLflow, Hugging Face Hub, or Weights & Biases.

- Previous experience in customer-facing AI/ML consulting, solution engineering, or forward-deployed engineering roles.

- Open-source contributions or technical publications related to AI, ML, or observability.