{"id":3244168,"url":"https://alion.io/job/auxoai-sr-applied-scientist-agents","title":"Sr. Applied Scientist - Agents","company":{"id":46892,"name":"AuxoAI","domain":"auxoai.com","url":"https://alion.io/company/auxoai","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Zoho Recruit","truth_index":{"grade":"A","score":89,"open_postings":9,"ghost_share":0,"stale_share":0.222,"repost_share":0,"time_to_fill_p50_days":76,"computed_at":"2026-10-27T05:46:45Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","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","Gurgaon, India","Mumbai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":25000,"max_usd":51000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":41},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Distillation","optional":false},{"name":"Python","optional":false},{"name":"Reinforcement Learning","optional":true},{"name":"Reward Modeling","optional":true}],"status":"live","first_seen_at":"2026-10-25T18:35:30Z","employer_posted_date":null,"last_verified_at":"2026-10-25T18:35:30Z","board_verified":false,"closed_at":null,"days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"AuxoAI is hiring a Senior Applied Scientist to design and deploy production-grade AI agents capable of structured reasoning, planning, and decision-making.\nThis role focuses on building intelligent agent systems that combine LLM-based reasoning with classical planning, search algorithms, and optimization techniques. The ideal candidate will develop robust agent architectures that operate reliably in real-world environments with constraints around latency, cost, uncertainty, and limited context windows.\n\nYou will work on advanced AI systems that power autonomous workflows, decision engines, and tool-driven agent ecosystems. You will work on problems where existing architectures may not be sufficient, and you will be expected to experiment with new approaches that combine machine learning, graph algorithms, and classical AI techniques to build reliable, production-grade systems.\n\nResponsibilities: \nDesign and architect modular AI agent frameworks incorporating skill decomposition, tool orchestration, and persistent state tracking.\nImplement planning and search algorithms such as Monte Carlo Tree Search (MCTS), beam search, A search, heuristic search, and graph-based planning approaches to support complex decision-making tasks.\nDevelop decision-making loops that balance trade-offs between exploration vs. exploitation, cost vs. accuracy, and latency vs. reasoning depth.\nBuild structured memory systems including episodic memory stores, semantic memory layers, and vector-based memory with optimised retrieval strategies.\nDesign tool-calling architectures with strong execution validation, retry mechanisms, and failure recovery strategies.\nDevelop evaluation frameworks to measure agent performance using task success metrics, rollout simulations, and multi-sample validation approaches.\nImprove agent performance through techniques such as distillation, synthetic trajectory generation, prompt compression, and context pruning.\nDeliver production-ready agent systems that meet operational requirements around reliability, cost efficiency, throughput, and observability.\n\nRequirements: \n2-5 years of experience building machine learning or AI systems in production environments.\nStrong experience implementing search or planning algorithms beyond basic use cases, including tree search or heuristic-based planning approaches.\nHands-on experience with Monte Carlo Tree Search (MCTS) or related decision-making frameworks.\nStrong understanding of state-space representations, heuristic design, and decision boundary trade-offs.\nExperience building or extensively customising agent frameworks for real-world applications.\nHands-on experience designing tool-use or function-calling architectures under practical system constraints.\nStrong Python engineering skills with a focus on scalable and reliable system design.\n\nNice to Have: \nExperience with reinforcement learning techniques such as policy gradients, value estimation, or reward modelling.\nExperience building multi-agent or collaborative agent systems.\nExperience designing evaluation frameworks for agent robustness and reliability.\nExperience optimising LLM inference pipelines for latency, throughput, and cost efficiency.\nFamiliarity with distributed task orchestration systems and large-scale AI workflow management.\nSkills\nAI, Python, artificial intelligence, machine learning, planning, 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