{"id":4091353,"url":"https://alion.io/job/random-trees-randomtrees-lead-ai-engineer","title":"RandomTrees - Lead AI Engineer","company":{"id":3801016,"name":"Random Trees","domain":"randomtrees.com","url":"https://alion.io/company/random-trees","size_band":"201-500","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":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Chennai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Azure AKS","optional":false},{"name":"bandit","optional":false},{"name":"Computer 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and measurable business impact across text, image, and multimodal experiences.\n\nYou will set technical direction and lead end-to-end AI solutions across matching, multimodal retrieval, ranking, computer vision, vision-language modeling, and GenAI-powered experiences from problem framing and research through deployment, evaluation, and iteration at scale.\n\nYou will also mentor engineers and scientists, establish reusable technical patterns, and influence cross-functional product and platform roadmaps.\n\nCore Responsibilities\n\n Set technical direction and lead research, experimentation, and production delivery for ML models spanning product matching, semantic/visual retrieval, search relevance, and image understanding.\n Architect and optimize multi-stage retrieval and ranking pipelines across text, image, and fused multimodal representations.\n Develop, fine-tune, and evaluate computer vision, vision-language, and multimodal models.\n Create rigorous offline evaluation, benchmarking, and online experimentation frameworks.\n Apply reinforcement learning, learning-to-rank, or bandit methods for personalization.\n Build and deploy GenAI and agentic AI systems.\n Lead model and data strategy for multimodal systems, ensuring robustness, bias mitigation, and safety.\n Productionize ML systems using Databricks and AKS, designing for latency, throughput, and observability.\n Establish reusable architectures and MLOps/LLMOps practices.\n\nRequired Qualifications\n\n 6+ years of experience in machine learning, applied science, or ML engineering, including leading complex initiatives.\n 2+ years of hands-on experience developing and fine-tuning computer vision models.\n Deep expertise in at least two of: Search/Retrieval/Ranking, Computer Vision, or Vision-Language/Multimodal models.\n Strong proficiency in Python and PyTorch or equivalent.\n Hands-on experience with Databricks, distributed pipelines, and Kubernetes/AKS.\n Demonstrated technical leadership and 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