{"id":3151785,"url":"https://alion.io/job/telence-solutions-data-scientist-llm-fine-tuning","title":"Data Scientist LLM Fine - Tuning","company":{"id":4465903,"name":"Telence Solutions","domain":"telencesolutions.com","url":"https://alion.io/company/telence-solutions","size_band":null,"is_staffing_agency":false,"employer_type":"staffing","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":"internship","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":["India"],"countries":["IN"],"hiring_countries":["IN"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":17000,"max_usd":47000,"period":"year","method":"role_country_seniority_unknown","sample_n":171},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"DeepSeek","optional":false},{"name":"DeepSpeed","optional":false},{"name":"Fine-tuning","optional":false},{"name":"FSDP","optional":false},{"name":"Hallucination","optional":false},{"name":"Hugging Face","optional":false},{"name":"Linux","optional":false},{"name":"Llama","optional":false},{"name":"LLM","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"LoRA","optional":false},{"name":"Machine Learning","optional":false},{"name":"Mistral","optional":false},{"name":"MLFlow","optional":false},{"name":"PEFT","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"QLoRA","optional":false},{"name":"Qwen","optional":false},{"name":"RAG","optional":false},{"name":"SFT","optional":false},{"name":"Synthetic Data","optional":false},{"name":"Time Series","optional":false},{"name":"Transformers","optional":false},{"name":"TRL","optional":false},{"name":"vLLM","optional":false},{"name":"AI Agents","optional":true},{"name":"Grafana","optional":true},{"name":"Knowledge Graph","optional":true},{"name":"MPLS","optional":true},{"name":"Multi-Agent Systems","optional":true},{"name":"Prometheus","optional":true}],"status":"closed","first_seen_at":"2026-10-23T12:35:00Z","employer_posted_date":null,"last_verified_at":"2026-10-28T18:35:45Z","board_verified":false,"closed_at":"2026-10-28T18:35:45Z","days_open":5,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":5},"description":"We're looking for a Data Scientist to own how the language models inside the ANE product are adapted to the realities of network operations. You'll turn real operational data (alarms, syslogs, telemetry, configurations, runbooks and engineers' decisions) into training and evaluation datasets. You'll then fine-tune open-weight models that run entirely on customer premises. Your work directly improves how accurately ANE explains faults, how well it follows a customer's playbooks, and how reliably it supports engineers.\n\nResponsibilities: \nCurate operational data: build datasets from network alarms, syslogs, SNMP and streaming telemetry, configuration data, incident history and runbooks, including cleaning, deduplication, labelling, and anonymisation of customer data.\nTurn engineer decisions into training signal: ANE records every proposed fix and whether an engineer approved, edited or rejected it. Use that record to build supervised and preference datasets.\nFine-tune open-weight models: retrain and fine-tune LLMs and smaller language models (for example, Llama, Mistral, Qwen or DeepSeek) using LoRA/QLoRA, supervised fine-tuning and preference-optimisation methods, sized for customer GPU hardware.\nBuild evaluation harnesses: measure domain accuracy, hallucination rates, tool-calling reliability and faithfulness to runbooks, so that no model ships without passing defined quality gates.\nImprove retrieval: fine-tune embedding models and tune RAG pipelines over vendor documentation and customer playbooks.\nPrepare models for on-premises deployment: handle quantisation, packaging and versioning so models run efficiently with local inference servers (for example, vLLM) in air-gapped environments.\nSupport forecasting: contribute time-series models for capacity and bandwidth forecasting and device-health trends.\nRun the model lifecycle: maintain reproducible training pipelines, model registries (for example, MLflow), model cards and governance documentation.\nWork across the team: collaborate with our AI architect, software developers and network engineers, who carry more than 30 years of experience building carrier and enterprise networks.\n\nRequirements: \nStrong working knowledge in data science or machine learning, including hands-on LLM fine-tuning.\nStrong Python and PyTorch, plus experience with Hugging Face Transformers, PEFT and TRL.\nPractical experience building fine-tuning datasets, including data cleaning, labelling strategy and synthetic data generation.\nExperience designing LLM evaluations and benchmarks, not just training runs.\nComfort working on Linux GPU servers, with distributed training (DeepSpeed or FSDP) and model quantisation.\nExperience with RAG systems, embedding models and vector databases.\nClear communication and the judgment to know when a model isn't ready to ship.\n\nNice to Have: \nExperience with network or IT operations data: syslog, SNMP, streaming telemetry, alarms or trouble tickets.\nFamiliarity with IP/MPLS, optical or SD-WAN networks, or with network management platforms such as Nokia NSP, Cisco Crosswork, Juniper Paragon, Grafana or Prometheus.\nExperience deploying models in air-gapped or highly regulated environments.\nTime-series forecasting and anomaly detection experience.\nKnowledge of agentic AI, multi-agent systems or knowledge graphs.\nSkills\nLLM, PyTorch, Python, large language 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