{"id":2913660,"url":"https://alion.io/job/quicklaunch-senior-aiml-engineer-unifyed","title":"Senior AI/ML Engineer - Unifyed","company":{"id":1880510,"name":"QuickLaunch","domain":"quicklaunch.io","url":"https://alion.io/company/quicklaunch","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Zoho Recruit","truth_index":{"grade":"B","score":75,"open_postings":5,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-29T05:49:05Z"}},"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":"structured","locations":["Gurgaon, 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Search","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"Vertex AI","optional":false},{"name":"Weaviate","optional":false},{"name":"Weights & Biases","optional":false},{"name":"PostgreSQL","optional":true}],"status":"live","first_seen_at":"2025-12-10T00:00:00Z","employer_posted_date":"2025-12-10","last_verified_at":"2026-10-29T15:02:52Z","board_verified":true,"closed_at":null,"days_open":323,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":323},"description":"Job Title: Senior AI/ML Engineer \nExperience Level: 6+ Years \nEmployment Type: Full-Time \nLocation: Gurugram, Sector 33 \nShift Timings: 12:00 PM - 9:00 PM IST\nAbout the Role: \nWe are looking for a hands-on Senior AI/ML Engineer who can own the full lifecycle of machine learning\nsolutions - from problem definition and data modelling to training, deployment, monitoring, and\ncontinuous improvement.\nYou should be comfortable working with messy real-world data, designing robust data models &\nfeatures, building and training models, and shipping them to production with proper MLOps practices.\nYou must also be aware of the current AI/ML landscape (LLMs, embeddings, vector search, modern\ntooling) and know when to use what.\nKey Responsibilities:\n\nEnd-to-End Solution Ownership\n\nWork with product / domain stakeholders to understand business problems and define ML usecases \n\nTranslate requirements into data & model design, success metrics, and clear technical plans \nOwn the full pipeline: data ingestion → cleaning → feature engineering → model training →evaluation → deployment → monitoring\n\nData Modelling & Feature \nEngineeringDesign and maintain data models / schemas optimized for analytics and ML training (batch & real\ntime) \n\nPerform exploratory data analysis (EDA) and feature engineering to improve signal quality andmodel performance \n\nWork closely with data engineering to ensure reliable, well-documented datasets\nModel Training & Evaluation \nBuild, train, and tune models for tasks such as: prediction, classification, ranking, recommendations,anomaly detection, and NLP. \n\nUse appropriate techniques (traditional ML, deep learning, embeddings, LLMs) based on theproblem \n\nDefine and track offline and online metrics; run A/B tests or controlled experiments where applicable\nMLOps & Productionization\nBuild reproducible training pipelines (e.g., using MLflow, Airflow, Kubeflow, or similar tools) \nPackage and deploy models as APIs / microservices or batch jobs, using containers and cloudservices \n\nImplement monitoring, alerting, and logging for model performance, data drift, and system health \nManage model versions, rollouts, and rollback strategies\nAI/ML Architecture & Best Practices \nEvaluate and integrate modern AI tools: vector databases, embedding models, LLM APIs, RAGarchitectures, etc.\nEnsure solutions follow security, privacy, and compliance best practices (e.g., PII handling, access\ncontrol) \n\nWrite clear documentation for data flows, models, and services \nMentor junior engineers/data scientists and contribute to engineering standards and guidelines\nMust-Have Skills & Experience\nCore Technical Skills\n(6+ Years)Python Programming: Strong expertise in ML libraries (pandas, numpy, scikit-learn, PyTorch,\nTensorFlow) \n\nSQL & Databases: Solid SQL skills and hands-on experience with relational and NoSQL data stores \nProduction ML: Demonstrated experience shipping end-to-end ML projects to production (not justnotebooks / POCs) \n\nML Fundamentals: Deep understanding of supervised/unsupervised learning, evaluation metrics,overfitting, bias/variance, data leakage\n\nMLOps & DevOps \nSenior AI/ML EngineerExperiment tracking tools (MLflow, Weights & Biases) \n\nModel versioning and packaging (Docker, virtualenv, Conda)CI/CD pipelines for ML services \n\nInfrastructure as Code and containerization best practices\nCloud & Architecture \nProficiency with at 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