{"id":1956148,"url":"https://alion.io/job/brillio-senior-aiml-engineer-r01571454","title":"Data Scientist - R01571454","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":"2026-10-26T05:57:25Z"}},"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":["Bengaluru, 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embedding models, retrieval ranking, and context window management to maximize information accuracy and retrieval efficiency\nBuild and systematically iterate on prompt engineering layers, testing and refining prompts and chain-of-thought strategies to achieve consistent, high-quality outputs across diverse inputs\nImplement tool orchestration within agent workflows by integrating agents with databases, rule engines, validation systems, and formatting tools to automate complex tasks\nEstablish automated quality checks and validation layers to proactively catch issues and ensure high output reliability before human review\nInstrument solutions for measurement, collaborating with data scientists to develop evaluation frameworks and track solution performance against defined targets\nDeploy and maintain AI/ML solutions in production environments, focusing on reliability, monitoring, and edge case handling\nDesign and implement feedback loops that 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, LlamaIndex, or similar\nExpertise in prompt engineering and systematic prompt testing\nDeep understanding of RAG architectures, including embedding models, vector stores, retrieval strategies, and re-ranking\nExperience building multi-step agent workflows with tool use, branching logic, and robust error handling\nExperience with production deployment and monitoring of AI/ML solutions\nExperience with data pipeline tools and frameworks (KubeFlow, BentoML, Great Expectations, Evidently AI)\nPreferred Skills:\nExperience with multi-agent orchestration frameworks\nBackground in content generation, translation, or document processing solutions\nFamiliarity with fine-tuning LLMs or training reward models\nExperience implementing feedback loops or RLHF mechanisms\nExpertise in LLM cost optimization strategies such as model routing, caching, and prompt compression\nExperience with multi-modal AI systems including voice-to-text, document understanding, and image analysis\nExperience with evaluation frameworks for generative AI, including automated scoring and human evaluation protocols\nDesired Qualifications:\nBachelor's degree in Computer Science, Data Science, Information Technology, or a closely related discipline\nCertification in Machine Learning or Artificial Intelligence (e.g., TensorFlow Developer Certificate, AWS Certified Machine Learning Specialty)\nCertification in LLM or generative AI technologies (e.g., OpenAI Certified Engineer, Hugging Face Certified AI 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