{"id":4321000,"url":"https://alion.io/job/weekday-senior-ml-engineer","title":"Senior ML Engineer","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":80,"open_postings":123,"ghost_share":0,"stale_share":0.984,"repost_share":0,"time_to_fill_p50_days":5,"computed_at":"2026-11-14T05:58:10Z"}},"role":"AI/ML","role_family":"AI/ML","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":["Delhi, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"bandit","optional":false},{"name":"Databricks","optional":false},{"name":"GCP","optional":false},{"name":"LightGBM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Recommender Systems","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"Transfer Learning","optional":false},{"name":"XGBoost","optional":false}],"status":"live","first_seen_at":"2026-11-14T20:21:45Z","employer_posted_date":"2026-11-14","last_verified_at":"2026-11-14T20:21:45Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"'\n: - ( - )\nExperience: 4+ yrs\nLocation: NCR, Delhi, India\nAs a Senior ML Engineer, you will lead the design and deployment of cutting-edge recommendation and personalization solutions that drive user engagement across our digital ecosystems. You will collaborate closely with data scientists, product managers, and engineering teams to build high-throughput, low-latency recommendation engines that adapt in real time to user behavior and contextual signals. By leveraging graph-based match-scoring, sophisticated ranking models, and innovative cold-start strategies, you will enhance the relevance of content and product suggestions for millions of users worldwide. Your work will directly influence key metrics such as click-through rate, conversion rate, and user retention, while ensuring scalability, robustness, and maintainability of the underlying machine learning infrastructure.\nRequirements\nDesign, develop, and production-grade real-time recommendation pipelines that process millions of events per day.\nEngineer graph-based match-scoring algorithms to uncover latent user-item relationships and improve match quality.\nBuild and fine-tune ranking models using learning-to-rank, gradient-boosted trees, or deep neural network approaches.\nDevelop cold-start solutions for new users and items using hybrid collaborative filtering, content embeddings, and transfer learning.\nPerform feature engineering at scale, ensuring feature freshness, consistency, and low latency latency requirements.\nCollaborate with data engineering teams to optimize data ingestion, storage, and retrieval for recommendation workloads.\nConduct rigorous A/B testing, statistical analysis, and interpret model outputs to drive data-driven decisions.\nMentor junior ML engineers, sharing best practices, code review, and debugging techniques.\nStay abreast of industry research, publish findings, and iterate on emerging techniques such as reinforcement learning or multi-armed bandit frameworks.\nEnsure robust monitoring, logging, and alerting for model drift, latency, and error rates across all services.\n4+ years of hands-on experience building large-scale recommendation systems in a production environment.\nDeep expertise in personalization, ranking, and real-time recommender architectures.\nStrong background in graph theory, graph neural networks, and bipartite graph matching for recommendation tasks.\nProven track record of addressing cold-start challenges with innovative techniques and measurable impact.\nProficiency in Python, SQL, Spark/Databricks, and a major cloud platform (AWS, GCP, Azure).\nSolid experience with ML frameworks such as TensorFlow, PyTorch, LightGBM, and XGBoost.\nDemonstrated ability to translate metrics and business objectives into actionable ML solutions.\nExcellent communication skills, capable of presenting complex technical concepts to non-technical stakeholders.\nA collaborative mindset, comfortable working in cross-functional teams in a fast-paced, iterative environment.\nPassion for staying current with academic research and industry trends, and willingness to experiment with new 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