{"id":4167155,"url":"https://alion.io/job/mobileum-principal-aiml-engineer","title":"Principal AI/ML Engineer","company":{"id":2084586,"name":"Mobileum","domain":"mobileum.com","url":"https://alion.io/company/mobileum","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":"lead","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":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"ChatGPT","optional":false},{"name":"CI/CD","optional":false},{"name":"Copilot","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"NLP","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Spark","optional":false},{"name":"Synthetic Data","optional":false},{"name":"TensorFlow","optional":false},{"name":"Time Series","optional":false},{"name":"Tokenization","optional":false},{"name":"Transfer Learning","optional":false},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"GCP","optional":true},{"name":"LLM","optional":true}],"status":"live","first_seen_at":"2026-11-12T13:35:30Z","employer_posted_date":null,"last_verified_at":"2026-11-12T13:35:30Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"The AI/ML Development team executes Business Unit product roadmaps, delivering traditional machine learning solutions and implementing agentic AI capabilities where the business and products need them.\n\nWe are a technology vendor serving the Telecom industry, and we are looking for a Principal AI/ML Engineer to be a senior technical owner of the ML solutions behind our products. The role is a hybrid that combines data science (developing and validating models) and ML engineering (building the training and inference pipelines and services that run them in production).\n\nAs a vendor, we often build models without unrestricted access to customer production data and with limited ground truth. We need someone who can deliver reliable solutions under those constraints, using privacy-preserving data practices and realistic synthetic data.\n\nRoles & Responsibility:\nData science and model development\nFrame business problems as ML problems, then design, train, and validate models (supervised learning, time series, forecasting, anomaly detection), including feature engineering and experimentation.\nBuild solutions where ground truth is scarce (weak/semi-supervised and unsupervised methods, transfer learning, domain rules, simulation, targeted expert labeling) and design evaluation strategies that stay credible with limited labels.\nAnalyze model performance and drive improvements in accuracy, robustness, and drift handling.\nData privacy and synthetic data\nApply data anonymization and tokenization so telecom data can be used for development, testing, and training while meeting customer privacy and regulatory requirements.\nGenerate realistic synthetic data that reproduces the patterns and edge cases of production data, and validate its fidelity and usefulness.\nDefine reusable tooling and standards for privacy-preserving data handling.\nML engineering and production\nDesign and evolve end-to-end training and inference pipelines for the core product ML solutions, from data processing through deployment.\nBuild production inference pipelines in PySpark and inference services with REST endpoints, and tune Spark jobs to cut processing time and infrastructure cost at telecom data volumes.\nApply MLOps practices (monitoring, retraining, versioning, testing, deployment automation) that work across different customer environments.\nAI and agentic AI\nUse AI and agentic AI to improve development and operational processes (AI-assisted coding and review, testing, documentation, monitoring, data quality checks).\nEnhance traditional ML solutions with agentic capabilities where they add value, using shared platform components from the AI Innovation & Platforms team.\nTechnical leadership\nSet technical standards for code and model review, testing, and reusable components, including guardrails for AI-assisted workflows.\nMentor junior engineers and data scientists, and technically lead selected projects.\nWork with product, engineering, BU stakeholders, and telecom domain experts to keep delivery aligned with roadmap commitments.\n\nRequired Skills:\n[8+] years in applied ML, data science, or ML engineering, with a strong record of taking traditional ML solutions from problem framing to production.\nProven experience building and validating ML solutions with scarce or no ground truth.\nStrong hands-on experience with data anonymization and tokenization, and with generating and validating realistic synthetic data.\nStrong expertise in machine learning (supervised learning, time series, anomaly detection, forecasting, predictive analytics).\nWorking knowledge of deep learning, NLP, and generative AI/LLMs.\nDeep hands-on experience with PySpark/Spark, including performance tuning.\nProven experience building ML training pipelines, production inference pipelines, and REST-based inference services.\nStrong Python and software engineering practices (testing, code review, CI/CD), and a solid Python ML stack (e.g., scikit-learn, PyTorch or TensorFlow).\nExperience with MLOps and model lifecycle management (e.g., MLflow or similar).\nExperience using AI-assisted development tools such as GitHub Copilot, ChatGPT, or similar platforms to improve engineering productivity.\nExperience mentoring others and acting as a technical reference.\nBachelor's or Master's degree in Computer Science, Engineering, Data Science, Statistics, or a related field, or equivalent experience.\n\nPreferred Qualifications:\nExperience in the Telecom industry or with network and operations data.\nExperience as a vendor or solution provider delivering ML across multiple customer environments.\nExperience with differential privacy or other privacy-enhancing technologies, and with generative or simulation-based synthetic data.\nHands-on experience integrating agentic AI or LLM-based solutions alongside traditional ML.\nExperience with cloud platforms (e.g., Azure, AWS, GCP).\n\nRequired Competencies:\nTechnical Leadership & Mentoring\nProblem Framing & Ownership\nAnalytical & Critical Thinking\nCommunication of Technical Trade-offs\nCross-functional Collaboration\nPragmatic Decision-Making under Constraints\nLearning Agility & Innovation\nSkills\nML, Machine Learning, PySpark, Python, Spark","description_format":"text","description_chars":5174,"description_truncated":false,"requirements":{"experience_years_min":8,"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":["Mobile 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