{"id":3241891,"url":"https://alion.io/job/inmobi-machine-learning-ii-iii","title":"Machine Learning II / III","company":{"id":1289,"name":"InMobi","domain":"inmobi.com","url":"https://alion.io/company/inmobi","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"A","score":92,"open_postings":26,"ghost_share":0,"stale_share":0.308,"repost_share":0,"time_to_fill_p50_days":55,"computed_at":"2026-10-30T05:48:23Z"}},"role":"Data Science","role_family":"Data Science","seniority":"middle","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":{"min_usd":17000,"max_usd":45000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":21},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"GCP","optional":false},{"name":"Kubernetes","optional":false},{"name":"LightGBM","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"TensorFlow","optional":false},{"name":"TorchServe","optional":false},{"name":"Triton","optional":false},{"name":"Apache Kafka","optional":true},{"name":"Feature Store","optional":true},{"name":"Flink","optional":true},{"name":"Grafana","optional":true},{"name":"Prometheus","optional":true},{"name":"Ray","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-10-25T16:35:00Z","employer_posted_date":null,"last_verified_at":"2026-10-25T16:35:00Z","board_verified":false,"closed_at":null,"days_open":4,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":4},"description":"As an ML Engineer II, you will contribute to building and scaling InMobi's ML platform by developing reliable ML pipelines, training workflows, and serving systems. You will work closely with senior engineers to operationalise ML models and improve developer productivity for data scientists and ML engineers.\n\nResponsibilities: \nBuild and enhance components of a self-serve ML platform supporting training, evaluation, and inference workflows.\nDevelop and deploy ML models using PyTorch, TensorFlow, or LightGBM, supporting the end-to-end ML lifecycle from experimentation to production.\nBuild and operate scalable, low-latency models serving pipelines using Triton, TorchServe, or TensorFlow Serving.\nImplement MLOps workflows using MLflow for experiment tracking and Airflow (or similar) for orchestration.\nWork with Kubernetes-based ML workloads, including resource configuration, autoscaling, and reliability improvements.\nAdd observability (metrics, logs, dashboards) to monitor training and inference systems and improve service stability.\n\nRequirements: \n3-9 years of experience building and deploying production ML systems.\nStrong Python programming.\nHands-on experience with PyTorch / TensorFlow.\nFamiliarity with ML training, evaluation, and inference workflows.\nBasic experience with distributed systems or ML workloads.\nExposure to Kubernetes and cloud platforms (GCP preferred).\nUnderstanding of feature engineering and model monitoring concepts.\n\nGood to Have: \nExperience with Ray (training or tuning).\nKnowledge of feature stores and online/offline pipelines.\nFamiliarity with observability tools (Prometheus, Grafana).\nExposure to Kafka, Spark, or Flink.\nSkills\nML, PyTorch, Python, TensorFlow, machine 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