Auxo AI - AI Engineer - Machine Learning

Auxo AI; Hyderabad; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

AuxoAI is seeking experienced AI Engineers to design and deploy production-grade AI agents capable of structured reasoning, planning, and decision-making. You will build intelligent agent systems and predictive ML solutions powering real-world enterprise workflows.

The role focuses on modular AI agent frameworks, tool orchestration, memory systems, and scalable, secure deployments in cloud environments, with emphasis on reliability, cost efficiency, and observability.

Qualifications

• 4-10 years of experience building ML/AI systems in production environments.
• Hands-on experience training, evaluating, and deploying models with scikit-learn, XGBoost, or PyTorch.
• Strong experience building or customizing agent frameworks for real-world apps.
• Experience designing tool-use or function-calling architectures under practical constraints.
• Experience with cloud-native AI platforms, preferably GCP Vertex AI and Gemini.
• Experience integrating AI with enterprise data systems (ERP APIs, data lakehouses like Databricks).
• Strong understanding of RAG architectures, vector DBs, and retrieval strategies.
• Familiarity with real-time or streaming data processing patterns (Pub/Sub, Kafka).
• Strong Python engineering skills for scalable, reliable system design.

Responsibilities

• Design and architect modular AI agent frameworks with skill decomposition, tool orchestration, and persistent state tracking.
• Build and deploy ML models for prediction, classification, anomaly detection, and pattern recognition in production.
• Develop decision-making loops balancing exploration vs. exploitation, cost vs. accuracy, latency vs. depth.
• Create structured memory systems including episodic/semantic memory and vector-based retrieval.
• Design tool-calling architectures with validation, retries, and recovery strategies.
• Develop evaluation frameworks measuring agent/model performance using various metrics.
• Integrate AI agents and ML models with enterprise systems.
• Deliver production-ready AI systems meeting reliability, cost efficiency, throughput, observability, and security standards.

Skills

Python

ML in production

Scikit-learn

XGBoost

PyTorch

Agent frameworks

Tool-calling architectures

Cloud platforms

Tools

Databricks

Pub/Sub

Kafka

Job description

AuxoAI is hiring AI Engineers 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.

About The Role

AuxoAI is hiring AI Engineers 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.

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

• 4 - 10 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.
• Familiarity with real-time or streaming data processing patterns (Pub/Sub, Kafka, or equivalent).
• Strong Python engineering skills with a focus on scalable, reliable, and maintainable system design.

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.
• Prior experience in semiconductor, manufacturing, or industrial AI environments.

(ref:hirist.tech)