Data Scientist

About Telence Solutions

Telence Solutions is a multi-vendor network engineering, cybersecurity and AI operations company serving service providers and mission-critical industries. Our platform, the Autonomic Network Engine (ANE), is an AI-driven network management system.

The Role

This is a 12-month internship position for Data Scientists. The role focuses on adapting language models within ANE to network operations. You will transform operational data into training and evaluation datasets and fine-tune open-weight models for on-premises deployment.

Key Responsibilities

• Curate operational data: build datasets from network alarms, syslogs, SNMP and streaming telemetry, configuration data, incident history and runbooks, including cleaning, deduplication, labeling and anonymization.
• Turn engineer decisions into training signal: build supervised and preference datasets based on engineer interactions with proposed fixes.
• Fine-tune open-weight models: retrain and fine-tune LLMs and smaller language models (e.g., Llama, Mistral, Qwen, DeepSeek) using LoRA/QLoRA, supervised fine-tuning, and preference-optimization methods.
• Build evaluation harnesses: measure domain accuracy, hallucination rates, tool-calling reliability, and faithfulness to runbooks.
• Improve retrieval: fine-tune embedding models and tune RAG pipelines over vendor documentation and customer playbooks.
• Prepare models for on-premises deployment: handle quantization, packaging, and versioning for local inference servers (e.g., vLLM) in air-gapped environments.
• Support forecasting: contribute time-series models for capacity and bandwidth forecasting, and device-health trends.
• Run the model lifecycle: maintain reproducible training pipelines, model registries (e.g., MLflow), model cards, and governance documentation.
• Work across the team: collaborate with AI architects, software developers, and network engineers.

Requirements & Tech Stack

• Strictly from Masters or PhD programs from IIT, IISc, and IIIT.
• 4+ years in data science or machine learning, including 2+ years of hands-on LLM fine-tuning.
• Strong Python and PyTorch.
• Experience with Hugging Face Transformers, PEFT, and TRL.
• Practical experience building fine-tuning datasets, including data cleaning, labeling strategy, and synthetic data generation.
• Experience designing LLM evaluations and benchmarks.
• Comfort working on Linux GPU servers, with distributed training (DeepSpeed or FSDP) and model quantization.
• Experience with RAG systems, embedding models, and vector databases.
• Clear communication and good judgment.

Nice to Have

• Experience with network or IT operations data: syslog, SNMP, streaming telemetry, alarms, or trouble tickets.
• Familiarity with IP/MPLS, optical, or SD-WAN networks, or with network management platforms like Nokia NSP, Cisco Crosswork, Juniper Paragon, Grafana, or Prometheus.
• Experience deploying models in air-gapped or highly regulated environments.
• Time-series forecasting and anomaly detection experience.
• Knowledge of agentic AI, multi-agent systems, or knowledge graphs.