{"id":1956145,"url":"https://alion.io/job/brillio-senior-aiml-engineer-r01572335","title":"Data Scientist - R01572335","company":{"id":3063,"name":"Brillio","domain":"brillio.com","url":"https://alion.io/company/brillio","size_band":"501-1000","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":76,"open_postings":44,"ghost_share":0,"stale_share":0.955,"repost_share":0,"time_to_fill_p50_days":29,"computed_at":"2026-10-24T06:02:35Z"}},"role":"Data Science","role_family":"Data Science","seniority":"junior","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":10500,"max_usd":24000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":13},"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Chain-of-Thought","optional":false},{"name":"Function Calling","optional":false},{"name":"LangChain","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"AWS","optional":true},{"name":"Machine Learning","optional":true},{"name":"Multi-Agent Systems","optional":true},{"name":"Multimodal AI","optional":true},{"name":"Reward Modeling","optional":true},{"name":"RLHF","optional":true},{"name":"TensorFlow","optional":true}],"status":"live","first_seen_at":"2026-10-06T10:45:43Z","employer_posted_date":"2026-10-06","last_verified_at":"2026-10-24T21:02:55Z","board_verified":true,"closed_at":null,"days_open":18,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":18},"description":"Senior AI/ML Engineer\nJob requirements\nExperience Range: 2 to 4 years of experience, including at least 2 years specifically focused on developing LLM-based applications, RAG systems, or AI agent workflows Key Responsibilities:\nDesign and build end-to-end AI agent workflows, from initial prompt design through production deployment, ensuring scalable and reliable solutions\nDevelop and optimize Retrieval-Augmented Generation (RAG) pipelines by implementing chunking strategies, embedding models, retrieval ranking, and context window management for precise information retrieval\nBuild and iterate on prompt engineering layers, systematically testing and refining prompts and chain-of-thought strategies to deliver consistent outputs across diverse inputs\nImplement tool orchestration within agent workflows, integrating agents with databases, rule engines, validation systems, and formatting tools for seamless operation\nEstablish automated quality checks and validation layers to proactively identify and resolve issues before outputs reach human reviewers\nCollaborate with Data Scientists to instrument solutions for measurement, developing evaluation frameworks and tracking solution performance against defined targets\nDeploy, monitor, and maintain AI/ML solutions in production environments, ensuring reliability, scalability, and robust error handling\nDesign and implement feedback loops to capture expert review data and translate it into measurable improvements in agent performance\nRequired Skills:\nAdvanced proficiency in Python\nHands-on experience with LLM frameworks such as LangChain or LlamaIndex\nExpertise in prompt engineering for systematic testing and iteration\nDeep understanding of RAG architectures including embedding models, vector stores, retrieval strategies, and re-ranking\nExperience building multi-step agent workflows with tool use and branching logic\nExperience deploying and maintaining AI/ML solutions in production environments\nExperience with data pipeline development for feeding AI systems\nPreferred Skills:\nExperience with multi-agent orchestration frameworks\nBackground in content generation, translation, or document processing solutions\nFamiliarity with feedback loops, RLHF, or reward model training\nKnowledge of multi-modal AI systems including voice-to-text, document understanding, and image analysis\nExperience with evaluation frameworks for generative AI and automated scoring\nExperience with LLM cost optimization strategies such as model routing, caching, and prompt compression\nDesired Qualifications:\nBachelor's degree in Computer Science, Data Science, Information Technology, Statistics, or a closely related discipline\nCertification in Machine Learning or Artificial Intelligence (e.g., TensorFlow Developer Certificate, AWS Certified Machine Learning - 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