{"id":4028426,"url":"https://alion.io/job/allstate-data-scientist-senior-consultant-ii","title":"Data Scientist Senior Consultant II","company":{"id":4796,"name":"Allstate","domain":"allstate.com","url":"https://alion.io/company/allstate","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Paylocity","truth_index":null},"role":"Data Science","role_family":"Data Science","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":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon CloudWatch","optional":false},{"name":"Amazon S3","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"AWS Lambda","optional":false},{"name":"CI/CD","optional":false},{"name":"Datadog","optional":false},{"name":"Embeddings","optional":false},{"name":"Explainable AI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"Machine Learning","optional":false},{"name":"Microsoft Fabric","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Semantic Search","optional":false}],"status":"live","first_seen_at":"2026-11-10T17:35:30Z","employer_posted_date":null,"last_verified_at":"2026-11-10T17:35:30Z","board_verified":false,"closed_at":null,"days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"In this role, you will design and build AI-driven Insight Studio for the Scholar product, transforming large-scale customer conversations into actionable, real-time insights. You will hands-on build and productionize AI systems, integrating LLMs, RAG pipelines, and AI agents into scalable, reliable applications that directly power business decisions.\n\nKey Responsibilities:\nTranslate complex business problems into end-to-end AI solutions, leveraging machine learning and Generative AI techniques aligned with product and business goals.\nDesign, build, and operate production-grade AI systems for conversational understanding, summarization, classification, and insight generation from large-scale customer interaction data.\nArchitect and implement Retrieval-Augmented Generation (RAG) pipelines, integrating structured and unstructured data sources to deliver context-aware, grounded, and explainable AI outputs.\nBuild and optimize vector search and embeddings-based systems, enabling semantic search, similarity matching, and efficient information retrieval at scale.\nDesign and implement AI agent workflows, orchestrating multi-step reasoning, tool usage, and decision-making using frameworks such as LangChain, LangGraph, or similar.\nApply prompt engineering and LLM orchestration techniques to improve output quality, consistency, latency, and cost efficiency in production environments.\nIntegrate foundation models via platforms such as AWS Bedrock, including fine-tuning, evaluation, and deployment strategies tailored to business use cases.\nBuild and maintain scalable data pipelines and distributed processing systems to support real-time and batch AI workloads.\nOwn end-to-end lifecycle of AI/ML solutions, including data preparation, model development, evaluation (including LLM evaluation and guardrails), deployment, and continuous improvement.\nImplement MLOps and LLMOps practices, including CI/CD pipelines, versioning, monitoring, and automated evaluation for both traditional ML and AI systems.\nMonitor and optimize AI systems using observability tools (e.g., Datadog, CloudWatch), ensuring performance, reliability, and cost control in production.\nAnalyze model outputs, communicate findings, and iterate on solutions to continuously improve business impact and user experience.\n\nEssential Skills:\n\nData Science & AI Expertise:\nStrong proficiency in Python for AI/ML\nProven experience building and scaling production-grade AI/ML systems\nDeep understanding of data pipelines, distributed systems, and scalable architectures\nStrong grounding in MLOps / LLMOps workflows and lifecycle management\nPractical experience building applications using:\nLarge Language Models (LLMs) (prompting, fine-tuning, orchestration)\nRetrieval-Augmented Generation (RAG) architectures\nEmbeddings and vector search systems\nAI agents and multi-step reasoning workflows\nStrong understanding of LLM evaluation, guardrails, and responsible AI practices\n\nFrameworks & Tooling:\nHands-on experience with:\nLangChain, LangGraph, LlamaIndex or similar orchestration frameworks\nExperience designing modular, reusable AI pipelines and components\n\nData & Storage:\nExperience with vector databases and modern data storage systems\nFamiliarity with handling large-scale structured and unstructured datasets\n\nCloud & AI Platforms:\nExperience with:\nAmazon SageMaker or similar ML platforms\nAWS Bedrock for foundation model access and deployment\nAWS services such as Lambda, S3, ElastiCache\nExperience deploying LLM-powered applications in cloud-native architectures\n\nObservability & Reliability:\nExperience with:\nDatadog, AWS CloudWatch\nStrong ability to monitor, debug, and optimize AI systems in production\n\nAdditional Platforms:\nExperience with Microsoft Fabric or similar analytics platforms\nSkills\nGen AI, Generative AI, LLM, Large Language Models, Python, RAG, Retrieval-Augmented 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