{"id":3176884,"url":"https://alion.io/job/airtel-wireless-machine-learning-engineer-data-science","title":"Machine Learning Engineer- Data Science","company":{"id":1062417,"name":"Airtel Wireless","domain":"airtelwireless.ca","url":"https://alion.io/company/airtelwireless","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Gurgaon, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":21000,"max_usd":51000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":32},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Machine Learning","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"TensorFlow","optional":false},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-10-23T17:28:05Z","employer_posted_date":null,"last_verified_at":"2026-10-23T17:28:05Z","board_verified":false,"closed_at":null,"days_open":5,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":5},"description":"Data Science (Machine Learning Engineer / MLE)\n\nAirtel is hiring for a MLE professional who is passionate to work in the area of financial services (fintech) business. \n\nEver dreamed of working with 2-5 trillion records / day on a 200+ petabyte base\nWant to improve experience of 400 million consumers using machine learning\nDo you want to work with one of world's largest telco and India's top brand\n\nIf so, we would like you to join us on an exciting journey to create significant impact using data. The mission of the Data Science team is to transform terabytes of data into robust models for personalization and discovery. \n\nYour work will directly impact the way millions of Indians use our Digital offerings. We would like you to join us on an exciting journey to create advanced ML products.\n\nResponsibilities\nDevelop and deploy machine learning models with a focus on quick, reliable experimentation to validate features and hypotheses. Our team values speed and quality in experimentation.\nAssist in building and maintaining machine learning pipelines, aiding in tasks such as data preparation, model training, and evaluation. \nCreate scalable and cost-effective inference pipelines using modern tools\nMonitor the health and performance of machine learning models, to ensure their effectiveness\nDrive innovation in our Gen AI ecosystem by developing autonomous systems for key tasks like personalized recommendations, cross-sell and customer interactions\nContribute to building reusable ML/AI components, frameworks and best practices that enable deployment of solutions at scale.\nExplore and build Agentic AI use cases, including AI agents capable of reasoning, planning, tool/API usage, workflow orchestration and autonomous execution of business tasks.\nBe involved in technology research, capability building across newer technologies and tools in Machine Learning ecosystem at scale\n\nQualifications – As a successful applicant, you should have:\nB.Tech or MS in Computer Science \nDeep understanding of data structures and algorithms\nExpertise in Python, modern ML frameworks (Pyspark, PyTorch, TensorFlow) and MLops.\nExperience with large language models and prompt engineering\nExperience optimizing model inference speed and cost for large-scale deployments\nProven track record in deploying production-ready ML models\nGood understanding of Machine Learning algorithms\n3 to 6 years of experience in designing and deploying ML modeling and prediction pipelines\nAbility to contribute to multiple projects simultaneously and work in a fast-paced, collaborative and iterative 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