{"id":2625152,"url":"https://alion.io/job/weekday-aiml-engineer-4","title":"AI/ML Engineer","company":{"id":7097,"name":"Weekday","domain":"weekday.works","url":"https://alion.io/company/weekday","size_band":"51-200","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"B","score":81,"open_postings":108,"ghost_share":0,"stale_share":0.972,"repost_share":0,"time_to_fill_p50_days":5,"computed_at":"2026-10-15T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","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":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Embeddings","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"LLM","optional":false},{"name":"LoRA","optional":false},{"name":"Machine Learning","optional":false},{"name":"Neo4j","optional":false},{"name":"NLP","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"QLoRA","optional":false},{"name":"RAG","optional":false},{"name":"Recommender Systems","optional":false},{"name":"Synthetic Data","optional":false},{"name":"PEFT","optional":true}],"status":"live","first_seen_at":"2026-10-15T14:46:38Z","employer_posted_date":"2026-10-15","last_verified_at":"2026-10-16T01:43:38Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"'\n: - ( - )\nExperience: 3+ yrs\nLocation: Bengaluru\nWe are looking for a skilled and innovative AI/ML Engineer to build and scale intelligent AI systems focused on Large Language Models (LLMs), conversational AI, long-term memory, and personalization. The role involves developing advanced retrieval systems, adapting open-source models, and building domain-specific AI capabilities that deliver context-aware, accurate, and personalized user experiences.\nThe ideal candidate will combine strong Python programming skills with hands-on expertise in LLM engineering, Retrieval-Augmented Generation (RAG), vector databases, model fine-tuning, and production deployment.\nRequirements\nKey Responsibilities\nDesign and develop conversational AI systems with contextual understanding, long-term memory, and personalised response generation.\nBuild and optimise RAG pipelines using embeddings, vector databases, structured data, and knowledge retrieval techniques.\nDevelop memory systems for user profiling, information extraction, relevance scoring, retrieval, ranking, and context selection.\nIntegrate, deploy, and fine-tune open-source LLMs for specific product and domain requirements.\nDevelop specialised Small Language Models (SLMs) to support domain-specific reasoning and improve response quality.\nCreate domain-specific datasets and training pipelines for fine-tuning, model adaptation, and continuous learning.\nImplement LLM orchestration, prompt engineering, classification, ranking, and lightweight machine learning workflows.\nBuild evaluation frameworks to measure retrieval relevance, memory accuracy, reasoning quality, response consistency, and overall model performance.\nOptimise inference latency, compute utilisation, model reliability, and deployment costs.\nDevelop and maintain model-serving APIs, automated tests, monitoring systems, and production deployment workflows.\nInvestigate AI failures, analyse model outputs, and implement improvements based on measurable performance indicators.\nCollaborate with Product and Engineering teams to translate user requirements into scalable AI solutions.\nEvaluate emerging LLM technologies, retrieval architectures, and model optimisation techniques to improve system capabilities.\nWhat Makes You a Great Fit\n3+ years of experience in AI/ML engineering, machine learning, NLP, or production AI application development.\nStrong proficiency in Python and experience building reliable, production-grade AI systems.\nHands-on experience with LLMs, open-source models, embeddings, vector databases, and RAG architectures.\nUnderstanding of LLM fine-tuning, dataset preparation, model evaluation, and inference optimisation.\nExperience developing retrieval, conversational memory, context management, or personalisation systems.\nFamiliarity with LLM orchestration frameworks, prompt engineering, and multi-step AI pipelines.\nExperience deploying models through APIs and implementing testing, monitoring, and production support.\nStrong analytical and problem-solving skills, with the ability to evaluate AI improvements using measurable outcomes.\nKnowledge of LoRA/QLoRA, SLMs, knowledge graphs, Neo4j, or graph-based retrieval is an advantage.\nExperience with recommendation systems, ranking models, synthetic data generation, or automated evaluation pipelines is beneficial.\nFamiliarity with GPU inference optimisation and self-hosted open-source models is desirable.\nExperience in astrology, symbolic reasoning, or specialised domain-reasoning systems is an added advantage.\nStrong ownership, communication, and collaboration skills, with the ability to drive AI initiatives from experimentation through production.","description_format":"text","description_chars":3661,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Continuous learning"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-10-15T14:46:38Z"}],"visa":[],"liveness":{"score":52,"band":"ok","label":"Likely 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