{"id":2911704,"url":"https://alion.io/job/contiinex-senior-data-scientist-llm-slm","title":"Senior Data Scientist (LLM / SLM)","company":{"id":3835136,"name":"Contiinex","domain":"contiinex.com","url":"https://alion.io/company/contiinex","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":24000,"max_usd":50000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":41},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWQ","optional":false},{"name":"Bitsandbytes","optional":false},{"name":"Fine-tuning","optional":false},{"name":"GGUF","optional":false},{"name":"LLM","optional":false},{"name":"LoRA","optional":false},{"name":"NLP","optional":false},{"name":"PEFT","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"QLoRA","optional":false},{"name":"Transformers","optional":true}],"status":"live","first_seen_at":"2026-10-20T09:35:00Z","employer_posted_date":null,"last_verified_at":"2026-10-20T09:35:00Z","board_verified":false,"closed_at":null,"days_open":13,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":13},"description":"Responsibilities: \nFine-tuning pipelines with LoRA / QLoRA / PEFT on domain-specific healthcare corpora.\nSynthetic training data generation (JSONL) for clinical and billing NLP tasks.\nEvaluation design: precision, recall and task-specific rubrics for healthcare language.\nQuantisation and inference optimisation (GGUF, AWQ, bitsandbytes) for GPU-constrained deployments.\nSystematic, reproducible prompt engineering.\nBenchmarking SLMs against frontier models on narrow, well-defined tasks.\n\nRequirements: \nM. 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