{"id":3266750,"url":"https://alion.io/job/weekday-senior-data-scientist-3","title":"Senior Data Scientist","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":92,"ghost_share":0,"stale_share":0.978,"repost_share":0,"time_to_fill_p50_days":6,"computed_at":"2026-10-31T05:58:55Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Mumbai, India","Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":1800000,"max":5000000,"currency":"INR","period":"year","gross":null,"usd_annual":52800},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Speech Recognition","optional":false},{"name":"Text-to-Speech","optional":false},{"name":"Hugging Face","optional":true},{"name":"NLP","optional":true},{"name":"PyTorch","optional":true},{"name":"SQL","optional":true},{"name":"TensorFlow","optional":true}],"status":"live","first_seen_at":"2026-10-26T09:36:16Z","employer_posted_date":"2026-10-26","last_verified_at":"2026-11-01T03:15:06Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"description":"This role is for one of Weekday’s clients\nSalary range: Rs 1800000 - Rs 5000000 (ie INR 18 - 50 LPA)\nMin Experience: 3+ years\nLocation: MUMBAI, Bengaluru, Karnataka, India\nJobType: full-time\nWe are looking for an experienced Senior Data Scientist to design, develop, and deploy Generative AI solutions, with a strong focus on voice bots and conversational AI systems. The ideal candidate will have hands-on experience building production-ready AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), speech technologies, and advanced machine learning techniques.\nIn this role, you will build intelligent voice-first systems for use cases such as sales, collections, and customer experience (CX). You will be responsible for improving conversational quality, reducing response latency, enhancing model reliability, and developing scalable AI pipelines. You will collaborate with product, machine learning engineering, and platform teams to transform research and experimentation into reliable, production-grade solutions.\nRequirements\nKey Responsibilities\n1. Generative AI and Conversational AI Development\nDesign, build, and deploy Generative AI-powered voice bots and conversational AI applications for enterprise use cases.\nDevelop intelligent conversational systems that support natural, context-aware, and reliable interactions.\nEvaluate and integrate LLMs such as GPT, LLaMA, and other suitable foundation models.\nDevelop prompt engineering strategies, fine-tuning approaches, and context-management techniques to improve model performance.\n2. Voice Pipeline Engineering\nDesign and optimise end-to-end voice processing pipelines involving Speech-to-Text (STT), Large Language Models, and Text-to-Speech (TTS).\nImprove response latency, speech quality, conversational flow, and overall user experience.\nIntegrate speech recognition and speech synthesis technologies with LLM-powered applications.\nIdentify and resolve performance bottlenecks across voice processing and inference workflows.\n3. RAG and LLM Optimisation\nBuild and maintain RAG pipelines to enable accurate, context-aware responses using relevant knowledge sources.\nDevelop retrieval strategies, prompt templates, and contextual grounding mechanisms to improve response quality.\nEvaluate and optimise LLM performance for voice-first applications and domain-specific business requirements.\nImprove tool-calling accuracy, response consistency, and the reliability of multi-step conversational workflows.\nExperiment with model fine-tuning and other optimisation techniques to enhance performance.\n4. Conversation Design and Workflow Development\nDesign conversational workflows, dialogue systems, intents, and interaction patterns for voice-based applications.\nImprove conversation handling, context retention, error recovery, and response relevance.\nDevelop tool-augmented LLM applications and agentic workflows that can execute tasks and interact with external systems.\nAnalyse conversation outcomes and user interactions to identify opportunities for improvement.\n5. Data Processing and Model Evaluation\nWork with large-scale conversational datasets, including text, call transcripts, and audio data.\nDevelop data processing, cleaning, preparation, and evaluation pipelines for AI model development.\nEstablish evaluation frameworks to measure response quality, relevance, latency, accuracy, and task completion.\nConduct experiments and analyse model outputs to identify performance gaps and validate improvements.\n6. AI Safety, Reliability, and Production Deployment\nImplement strategies to mitigate hallucinations, bias, unsafe responses, and privacy risks.\nImprove the reliability, robustness, and consistency of AI-powered conversational systems.\nCollaborate with Product, ML Engineering, and Platform teams to deploy and maintain AI solutions in production.\nMonitor system performance and continuously improve models, pipelines, and conversational experiences.\nExplore emerging technologies, including multimodal AI models and advanced agentic systems.\nMust-Have Skills\nGenerative AI: Strong practical understanding of LLMs and experience developing production-oriented Generative AI applications.\n Retrieval-Augmented Generation (RAG): Experience building retrieval pipelines and grounding LLM responses in relevant information.\nConversational AI: Hands-on experience developing and deploying voice bots or conversational AI systems in production.\nLLM Optimisation: Knowledge of prompt engineering, model evaluation, fine-tuning, and improving response quality.\nSpeech Technologies: Understanding of STT and TTS systems and their integration into end-to-end voice pipelines.\nPython Programming: Strong coding skills for AI development, experimentation, data processing, and deployment.\n Machine Learning: Solid understanding of machine learning and deep learning concepts.\nGood-to-Have Skills\nExperience with PyTorch, TensorFlow, and Hugging Face.\nFamiliarity with GPT, LLaMA, and other open-source or commercial LLMs.\nExperience with SQL and NoSQL databases and large-scale data handling.\nKnowledge of multimodal AI, AI agents, tool calling, and workflow orchestration.\nExperience optimising inference latency, model performance, and production AI systems.\nFamiliarity with responsible AI practices, privacy controls, and conversational safety mechanisms.\nExperience building AI solutions for sales automation, collections, customer support, or customer experience.\nRequired Qualifications and Experience\n3-10 years of experience in Data Science, Natural Language Processing (NLP), Conversational AI, Generative AI, or a related field.\nDemonstrated hands-on experience building and deploying voice bots or conversational AI systems in production.\nStrong understanding of LLM architectures, RAG pipelines, prompt engineering, and fine-tuning techniques.\nPractical experience designing speech-enabled AI applications using STT and TTS components.\nProficiency in Python and experience working with structured and unstructured data.\nFamiliarity with deep learning frameworks and modern AI development tools.\nStrong analytical, problem-solving, and experimentation skills.\nAbility to collaborate with cross-functional teams and translate business requirements into effective AI 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