{"id":4079862,"url":"https://alion.io/job/bmw-techworks-india-mid-level-python-ai-developer","title":"Mid-Level Python AI Developer","company":{"id":4445239,"name":"BMW TechWorks India","domain":"bmwtechworks.in","url":"https://alion.io/company/bmw-techworks-india","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Embeddings","optional":false},{"name":"Fine-tuning","optional":false},{"name":"LLM","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Tokenization","optional":false},{"name":"LLMOps","optional":true}],"status":"live","first_seen_at":"2026-11-11T16:50:26Z","employer_posted_date":"2026-11-11","last_verified_at":"2026-11-11T16:50:26Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Mid-Level Python AI Developer\nExperience: 5-9 years\nRole Overview\nIndependently implement Python AI components and LLM applications, from data preparation and model adaptation through evaluation, inference and production integration. Understand how models work as well as how to use them.\nKey Responsibilities\nBuild maintainable Python code, data pipelines, backend services and APIs for AI applications.\nPrepare and validate datasets, create appropriate train/validation/test splits, prevent leakage and track data and experiment versions.\nImplement training and fine-tuning workflows for suitable existing models, including supervised or parameter-efficient fine-tuning with guidance on broader architecture decisions.\nRun reproducible experiments, inspect loss curves and metrics, diagnose errors and improve model quality.\nBuild LLM inference and application pipelines using tokenization, embeddings, retrieval, vector search and RAG where appropriate.\nDevelop evaluation datasets and automated checks for task performance, groundedness and hallucinations; compare changes against a baseline.\nIntegrate models through APIs and contribute to deployment, monitoring, latency and cost optimization.\nWrite tests, use version control, document decisions and protect confidential data in datasets, prompts, logs and outputs.\nRequired Skills\nStrong Python development and data processing skills, with practical ML/deep learning implementation experience.\nUnderstanding of optimization, loss functions, overfitting and validation, plus tokenization, embeddings, attention and transformer architecture fundamentals.\nAbility to distinguish pretraining from training/fine-tuning and inference, and explain when model adaptation or retrieval is appropriate.\nHands-on use of PyTorch or a comparable framework, transformer tooling, model evaluation and LLM application development.\nExperience with APIs, automated testing and reproducible software and experiment workflows.\nPreferred Skills\nExperience with parameter-efficient fine-tuning, vector databases, containerized deployment and MLOps/LLMOps tools.\nFamiliarity with monitoring model quality and application performance in 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