{"id":2920674,"url":"https://alion.io/job/auxoai-ai-engineer-fde","title":"AI Engineer (FDE)","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":95,"open_postings":7,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":76,"computed_at":"2026-10-20T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"hybrid","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":{"min_usd":19500,"max_usd":52000,"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":"AI Agents","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"Databricks","optional":false},{"name":"GCP","optional":false},{"name":"Gemini","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Tool Use","optional":false},{"name":"Vertex AI","optional":false},{"name":"XGBoost","optional":false},{"name":"Amazon SageMaker","optional":true},{"name":"Azure","optional":true},{"name":"Docker","optional":true},{"name":"Grafana","optional":true},{"name":"Helm","optional":true},{"name":"Kubernetes","optional":true},{"name":"Langfuse","optional":true},{"name":"LangSmith","optional":true},{"name":"OpenAI","optional":true},{"name":"Prometheus","optional":true},{"name":"Reinforcement Learning","optional":true},{"name":"Reward Modeling","optional":true},{"name":"Terraform","optional":true}],"status":"live","first_seen_at":"2026-09-29T00:00:00Z","employer_posted_date":"2026-09-29","last_verified_at":"2026-10-21T00:16:26Z","board_verified":true,"closed_at":null,"days_open":22,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":22},"description":"AuxoAIis hiring a Senior AI Engineer to design and deploy production-grade AI agents capable of structured reasoning, planning, and decision-making.\nThis role focuses on building intelligent agent systems and predictive ML solutions that power real-world enterprise workflows - going well beyond chatbot or RAG-style application development. The ideal candidate will design AI architectures that combine LLM-based reasoning with classical ML techniques, operatingreliably in production environments with constraints around latency, cost, data quality, and enterprise system integration.\nYou will work on advanced AI systems that power autonomous workflows, decision engines, and tool-driven agent ecosystems - spanning use cases in manufacturing, finance, supply chain, and enterprise operations.\nYou will also work on problems where existing architectures may not be sufficient andwill be expected to experiment with new approachesthat combine large language models, machine learning models, and data engineering patterns to build reliable, production-grade systems\nLocation - Mumbai/Bangalore/Hyderabad/Gurgaon (Hybrid - 3 Days a week in Office)\nResponsibilities: \nDesign and architect modular AI agent frameworks incorporating skill decomposition, tool orchestration, and persistent state tracking.\nBuild and deploy supervised and unsupervised ML models for prediction, classification, anomaly detection, and pattern recognition tasks in production environments.\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 and model performance using task success metrics, rollout simulations, model accuracy benchmarks, and multi-sample validation approaches.\nIntegrate AI agents and ML models with enterprise systems.\nDeliver production-ready AI systems that meet operational requirements around reliability, cost efficiency, throughput, observability, and enterprise security standards.\nRequirements\n3-7 years of experience building machine learning or AI systems in production environments.\nHands-on experience training, evaluating, and deploying ML models using frameworks such as scikit-learn, XGBoost, or PyTorch- including feature engineering, cross-validation, and model monitoring in production.\nStrong experience building or extensively customising agent frameworks for real-world applications.\nHands-on experience designing tool-use or function-calling architectures under practical system constraints.\nExperience working with cloud-native AI platforms, preferably GCP Vertex AI and Gemini, including model deployment, endpoint management, and AI pipeline orchestration.\nExperience integrating AI solutions with enterprise data systems - ERP APIs, data lakehouses(Databricks), or industrial data sources (MES, IoT/sensor streams).\nStrong understanding of RAG architectures, vector databases, and retrieval strategies - with the ability to go beyond retrieval into agentic reasoning and action.\nStrong Python engineering skills with a focus on scalable, reliable, and maintainable system design.\nExperience working with cloud-native infrastructure, APIs, containers, and distributed systems.\nExperience with production-grade logging, monitoring, metrics, alerting, and distributed system debugging.\nCandidates whose primary experience is limited to RAG pipelines or prompt engineering without hands-on ML model development or production agent delivery may not be a strong fit for this role.\nNice to Have\nExperience with Amazon Bedrock, SageMaker, or equivalent cloud AI services.\nExperience with Azure AI Foundry, Azure Machine Learning, Azure OpenAI, or other Azure AI services\nExperience with LangSmith, Langfuse, Grafana, Prometheus, or equivalent AI/LLM observability platforms.\nExperience with Kubernetes, Docker, Terraform, Helm, or other container orchestration and infrastructure-as-code technologies.\nExperience with GCP Vertex AI and Gemini ecosystem.\nExperience with reinforcement learning techniques such as policy gradients, value estimation, or reward modeling.\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 MLOpspractices including model versioning, drift monitoring, retraining pipelines, and model registries.\nFamiliarity with distributed task orchestration systems and large-scale AI workflow management.\nNote: Given the urgency of the role, we are currently prioritizing candidates who can join immediately or within 2 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