{"id":2902314,"url":"https://alion.io/job/kaaliber-ai-ml-engineer","title":"AI / ML Engineer","company":{"id":4451125,"name":"Kaaliber","domain":"kaaliber.com","url":"https://alion.io/company/kaaliber","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":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Noida, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":16500,"max_usd":44000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":31},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"Function 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human-review controls.\nGuide predictive-model development for classification, ranking, recommendation, forecasting and anomaly detection.\nEstablish dataset, experimentation, offline evaluation, online measurement and model-releasepractices.\nDefine quality, safety, latency and cost thresholds and ensure model changes are validated before production release.\nDesign MLOps and LLMOps capabilities covering versioning, deployment, tracing, drift, rollback and provider resilience.\nReview AI designs and code, create reference implementations and resolve complex modelling or production issues.\nMentor AI/ML engineers, improve technical depth and coordinate delivery with backend, frontend, QA, security and cloud teams.\nAssess external models and platforms while protecting data, limiting vendor dependence and controlling operational cost.\nCommunicate evidence, uncertainty, model limitations and technical trade-offs clearly to leadership and delivery teams.\n\nRequirements: \nAdvanced Python and strong foundations in machine learning, deep learning, statistics and experimental design.\nProven ownership of production LLM applications, including structured outputs, tool calling, retrieval and evaluation.\nExperience delivering classical ML systems using scikit-learn, PyTorch, TensorFlow or comparable frameworks.\nStrong understanding of precision, recall, ranking metrics, calibration, drift, bias, data leakage and error analysis.\nExperience with model deployment, versioning, feature or data pipelines, observability, and rollback.\nAbility to architect secure AI services and defend against prompt injection, unsafe tool use and sensitive-data leakage.\nStrong SQL and data-analysis skills with experience working on event, behavioural, or time-series datasets.\nTechnical leadership, mentoring, design review and stakeholder communication experience.\n\nPreferred experience: \nMulti-model routing, agent orchestration, vector retrieval and knowledge-graph experience.\nCausal inference, uplift modelling, recommendations, optimisation or contextual bandits.\nMultimodal models involving text, image, audio or video.\nCloud-based ML platforms, Kubernetes, distributed inference and GPU workload optimisation.\nExperience building shared AI platforms or standards used by more than one product team.\nSkills\nAI, LLM, ML, Python, artificial intelligence, large language model, large language models, machine learning","description_format":"text","description_chars":2642,"description_truncated":false,"requirements":{"experience_years_min":3,"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":[],"lifecycle":[{"event":"open","at":"2026-10-20T06:41:15Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring 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