{"id":1232423,"url":"https://alion.io/job/chayanikarecruittech-lead-data-scientist-aiml-engineering","title":"Lead Data Scientist - AI/ML Engineering","company":{"id":3800322,"name":"ChayanikaRecruittech","domain":"chayanika.in","url":"https://alion.io/company/chayanikarecruittech","size_band":null,"is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Schema","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":23000,"max_usd":50000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":28},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"Apache Kafka","optional":false},{"name":"CI/CD","optional":false},{"name":"Feature Store","optional":false},{"name":"Incident Management","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false}],"status":"live","first_seen_at":"2026-08-24T10:09:57Z","employer_posted_date":null,"last_verified_at":"2026-08-24T10:09:57Z","board_verified":false,"closed_at":null,"days_open":37,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":37},"description":"Roles & Responsibilities:\n\n- Lead AI Product Pods across Credit Risk, Fraud, and Collections functions.\n\n- Build and deploy production-scale Machine Learning systems for lending lifecycle decisioning.\n\n- Own complete ML lifecycle including feature engineering, model training, evaluation, deployment, monitoring, and continuous improvement.\n\n- Design scalable distributed ML infrastructure, feature stores, model registries, and MLOps pipelines.\n\n- Develop AI solutions for underwriting, portfolio risk monitoring, fraud detection, anomaly detection, and recovery optimization.\n\n- Drive model governance, monitoring, explainability, and compliance within BFSI regulatory standards.\n\n- Collaborate with Product, Risk, Engineering, Data, and Business teams to deliver AI-driven business outcomes.\n\n- Define AI platform architecture, operational excellence, SLAs, and incident management practices.\n\n- Build, mentor, and scale high-performing AI Engineering and Data Science teams.\n\nMandatory Requirements:\n\n- 10+ years of experience in Data Science, AI/ML or AI Engineering with hands-on experience building production-grade ML systems.\n\n- Hands-on experience building AI/ML solutions for Credit Risk, Fraud Risk Management (FRM), Collections & Recovery.\n\n- Current designation must be Lead or above.\n\n- Strong experience designing and deploying large-scale distributed Machine Learning systems.\n\n- Strong programming experience in Python, along with exposure to Spark, Kafka, Kubernetes, APIs/Microservices, CI/CD, Feature Store, Model Registry, and Distributed Computing.\n\n- Experience designing and deploying Credit Risk Models, Fraud Detection Models, Graph ML, Early Warning Systems, Portfolio Monitoring, Collections Optimization, Propensity Models, and Recovery Forecasting.\n\n- Proven experience leading AI/ML teams, owning end-to-end delivery, mentoring engineers, and managing production AI platforms.\n\n- Experience working under BFSI governance, including PII handling, auditability, model governance, compliance, secure-by-design architecture, and model risk management.\n\n- B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) are considered.\n\n- Age should be below 37 years.\n\n- CTC breakup: 75% fixed + 25% variable.\n\nPreferred Requirements:\n\n- Currently working as Lead / Principal / Engineering Manager / Associate Director / Director in reputed Product, FinTech, Banking, NBFC, or Global Capability Centers.\n\n- Experience building enterprise AI platforms using Graph ML, Vector Databases, LLM-enabled decisioning, distributed training frameworks, and large-scale AI infrastructure.\nSkills\nMachine Learning, Artificial Intelligence, Data Science, Data Scientist, Large Language Model, Python","description_format":"text","description_chars":2708,"description_truncated":false,"requirements":{"experience_years_min":10,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence"],"lifecycle":[{"event":"open","at":"2026-09-25T15:05:00Z"}],"liveness":{"score":9,"band":"cold","label":"Long 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