Senior AI Engineer - AI Agent Systems
AuxoAI
- Location
- Hybrid (Bangalore North, Karnataka)
- Employment
- Full-time
- Level
- Senior Level
About the Role
AuxoAI is an enterprise AI firm partnering with Fortune 500 companies to design and deploy agentic AI systems that increase innovation velocity. This role focuses on building production-grade AI agents and predictive ML solutions for autonomous enterprise workflows in sectors like manufacturing and finance.
Skills
Perks
- Hybrid Work
Full job details
AuxoAI is hiring a Senior AI Engineer to design and deploy production-grade AI agents capable of structured reasoning, planning, and decision-making.
This 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, operating reliably in production environments with constraints around latency, cost, data quality, and enterprise system integration.
You 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.
You will also work on problems where existing architectures may not be sufficient and will be expected to experiment with new approaches that combine large language models, machine learning models, and data engineering patterns to build reliable, production-grade systems
Responsibilities:
- Design and architect modular AI agent frameworks incorporating skill decomposition, tool orchestration, and persistent state tracking.
- Build and deploy supervised and unsupervised ML models for prediction, classification, anomaly detection, and pattern recognition tasks in production environments.
- Develop decision-making loops that balance trade-offs between exploration vs. exploitation, cost vs. accuracy, and latency vs. reasoning depth.
- Build structured memory systems including episodic memory stores, semantic memory layers, and vector-based memory with optimised retrieval strategies.
- Design tool-calling architectures with strong execution validation, retry mechanisms, and failure recovery strategies.
- Develop evaluation frameworks to measure agent and model performance using task success metrics, rollout simulations, model accuracy benchmarks, and multi-sample validation approaches.
- Integrate AI agents and ML models with enterprise systems.
- Deliver production-ready AI systems that meet operational requirements around reliability, cost efficiency, throughput, observability, and enterprise security standards.
Requirements
- 3-7 years of experience building machine learning or AI systems in production environments.
- Hands-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.
- Strong experience building or extensively customising agent frameworks for real-world applications.
- Hands-on experience designing tool-use or function-calling architectures under practical system constraints.
- Experience working with cloud-native AI platforms, preferably GCP Vertex AI and Gemini, including model deployment, endpoint management, and AI pipeline orchestration.
- Experience integrating AI solutions with enterprise data systems — ERP APIs, data lakehouses (Databricks), or industrial data sources (MES, IoT/sensor streams).
- Strong understanding of RAG architectures, vector databases, and retrieval strategies — with the ability to go beyond retrieval into agentic reasoning and action.
- Strong Python engineering skills with a focus on scalable, reliable, and maintainable system design.
Candidates 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.
Nice to Have
- Experience with reinforcement learning techniques such as policy gradients, value estimation, or reward modeling.
- Experience building multi-agent or collaborative agent systems.
- Experience designing evaluation frameworks for agent robustness and reliability.
- Experience optimising LLM inference pipelines for latency, throughput, and cost efficiency.
- Familiarity with MLOps practices including model versioning, drift monitoring, retraining pipelines, and model registries.
- Familiarity with distributed task orchestration systems and large-scale AI workflow management.