{"id":3281799,"url":"https://alion.io/job/opkey-llm-engineer","title":"LLM Engineer","company":{"id":2131879,"name":"Opkey","domain":"opkey.com","url":"https://alion.io/company/opkey","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":"Computer Vision","optional":false},{"name":"Fine-tuning","optional":false},{"name":"LLM","optional":false},{"name":"NLP","optional":false},{"name":"RAG","optional":false},{"name":"BERT","optional":true},{"name":"Few-Shot Learning","optional":true},{"name":"Knowledge Distillation","optional":true},{"name":"Model Distillation","optional":true},{"name":"Multimodal AI","optional":true},{"name":"Prompt Engineering","optional":true},{"name":"Python","optional":true},{"name":"PyTorch","optional":true},{"name":"TensorFlow","optional":true}],"status":"live","first_seen_at":"2026-10-26T16:35:00Z","employer_posted_date":null,"last_verified_at":"2026-10-26T16:35:00Z","board_verified":false,"closed_at":null,"days_open":3,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":3},"description":"Responsibilities: \nDesign, develop, and implement advanced LLM applications using state-of-the-art models and techniques.\nLead research initiatives in Retrieval-Augmented Generation (RAG) systems, improving the accuracy and efficiency of information retrieval and generation.\nConduct experiments on training and fine-tuning large language models for specific domains and tasks.\nCollaborate with cross-functional teams to integrate LLM solutions into production systems.\nResearch and apply modern computer vision techniques, including object detection and generative models.\nTransform unstructured data into structured formats for effective model training and analysis.\nDevelop and test interfaces for mobile applications, including analysing screenshots, visual features, and debugging bugs visually.\nStay up-to-date with the latest advancements in LLMs, NLP, computer vision, and deep learning, and apply new techniques to ongoing projects.\n\nRequirements: \n1+ years of experience in NLP, deep learning, and building LLM applications.\nExperience with modern computer vision techniques, such as object detection and generative models.\nExpertise in structuring unstructured data for model training and analysis.\nHands-on experience with testing and debugging visual features in mobile app interfaces using screenshots.\nPublication record in top-tier AI conferences (e. g., NeurIPS, ICML, ACL, EMNLP) is a plus.\nStrong programming skills in Python, with experience in deep learning frameworks such as PyTorch or TensorFlow.\nExtensive experience with transformer-based models (e. g., BERT, GPT, T5) and their applications.\nDemonstrated expertise in developing RAG systems and improving their performance.\nProficiency in training and fine-tuning large language models, including techniques like few-shot learning and prompt engineering.\nStrong background in NLP techniques, including text preprocessing, tokenisation, and embedding methods.\nFamiliarity with MLOps practices and tools for model versioning, experiment tracking, and deployment.\nExcellent problem-solving skills and ability to think creatively about AI solutions.\nStrong communication skills and ability to explain complex technical concepts to both technical and non-technical audiences.\nExperience with efficient training techniques for large models, such as distributed training and mixed-precision training.\nKnowledge of model compression techniques, including quantisation and distillation.\nFamiliarity with ethical AI practices and bias mitigation in language models.\nExperience with multi-modal models combining text with other data types (e. g., images, audio).\nContributions to open-source NLP or LLM projects.\nSkills\nLLM, NLP, RAG, large language model, retrieval augmented generation","description_format":"text","description_chars":2753,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Software Testing & 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