Auxo AI - Senior AI Engineer

Auxo AI; Gurugram District; Sur place; INR 3 500 000 - 6 500 000; Plein temps

Résumé du poste

AuxoAI is seeking a Senior AI Engineer to design and deploy production-grade AI agents capable of structured reasoning, planning, and decision-making for enterprise workflows.

You will architect modular AI agent frameworks, integrate with ERP APIs and data lakehouses, and build memory and tool-calling systems with robust observability and security.

Qualifications

• 3-8 years of experience building ML/AI systems in production.
• Strong Python programming and scalable system design.
• Hands-on experience with ML frameworks: scikit-learn, XGBoost, PyTorch.
• Experience with cloud-native AI platforms and tooling.
• Familiarity with vector databases and retrieval strategies.
• Experience integrating AI solutions with ERP APIs and data lakehouses.
• Ability to design tool-use architectures under real-world constraints.

Responsabilités

• Build production-grade ML models and AI agents for enterprise workflows.
• Design modular AI agent frameworks with skill decomposition and memory.
• Develop tool-calling architectures with validation, retry logic, and failure recovery.
• Create evaluation frameworks and performance benchmarks for agents/models.
• Integrate AI solutions with enterprise data systems and APIs.
• Ensure reliability, cost efficiency, throughput, observability, and security in production.
• Collaborate with teams to deploy scalable, maintainable systems on cloud platforms.

Connaissances

Python programming

Agent frameworks

RAG architectures

Production ML systems

Model deployment

Cloud platforms

Strong communication

Outils

scikit-learn

XGBoost

PyTorch

GCP Vertex AI

Databricks

Kubernetes

Docker

Description du poste

About The Role

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 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 3 - 8 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.
• 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.
• Experience integrating AI solutions with enterprise data systems - ERP APIs, data lakehouses (Databricks), or industrial data sources.
• Strong understanding of RAG architectures, vector databases, and retrieval strategies.
• Strong Python engineering skills with a focus on scalable, reliable, and maintainable system design.
• Experience working with cloud-native infrastructure, APIs, containers, and distributed systems.
• Experience with production-grade logging, monitoring, metrics, alerting, and distributed system debugging.

About The Role

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 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 3 - 8 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.
• 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.
• Experience integrating AI solutions with enterprise data systems - ERP APIs, data lakehouses (Databricks), or industrial data sources.
• Strong understanding of RAG architectures, vector databases, and retrieval strategies.
• Strong Python engineering skills with a focus on scalable, reliable, and maintainable system design.
• Experience working with cloud-native infrastructure, APIs, containers, and distributed systems.
• Experience with production-grade logging, monitoring, metrics, alerting, and distributed system debugging.

Nice To Have

• Experience with Amazon Bedrock, SageMaker, Azure AI Foundry, or equivalent cloud AI services.
• Experience with LangSmith, Langfuse, Grafana, Prometheus, or equivalent AI/LLM observability platforms.
• Experience with Kubernetes, Docker, Terraform, Helm, or other container orchestration technologies.
• Experience with reinforcement learning techniques, multi-agent systems, and MLOps the urgency of the role, we are currently prioritizing candidates who can join immediately or within 2 weeks.

(ref:hirist.tech)