AI Engineer

Zohorecruit; Bengaluru; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

AuxoAI is hiring an AI Engineer to design and deploy production-grade AI agents capable of structured reasoning, planning, and decision-making across enterprise workflows—from manufacturing to finance.

You will architect AI systems that combine LLM reasoning with classical ML, ensuring latency, cost, data quality, and security constraints are met in production. The role emphasizes hands-on ML development, tool orchestration, and scalable deployment.

Qualifications

• 3–10 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— including feature engineering, cross-validation, and model monitoring in production.
• Strong experience building or extensively customizing 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.
• Familiarity with real-time or streaming data processing patterns (Pub/Sub, Kafka, or equivalent) for inference on live operational data.
• Strong Python engineering skills with a focus on scalable, reliable, and maintainable system design.

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 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.

Skills

Python

Machine Learning

Production ML systems

LLM integration

Tool orchestration

Tools

Scikit-learn

XGBoost

PyTorch

Vertex AI

Databricks

Job description

AuxoAI is hiring aAI Engineer to design and deployproduction-grade AI agents capable of structured reasoning, planning, anddecision-making.

This role focuses on building intelligentagent systems and predictive ML solutions that power real-world enterpriseworkflows — going well beyond chatbot or RAG-style application development. Theideal candidate will design AI architectures that combine LLM-based reasoningwith classical ML techniques, operating reliably in production environmentswith constraints around latency, cost, data quality, and enterprise systemintegration.

You will work on advanced AI systems thatpower autonomous workflows, decision engines, and tool-driven agent ecosystems— spanning use cases in manufacturing, finance, supply chain, and enterpriseoperations.

You will also work on problems where existingarchitectures may not be sufficient and will be expected to experiment with newapproaches that combine large language models, machine learning models, anddata engineering patterns to build reliable, production-grade systems.

Responsibilities

• Design and architect modular AI agentframeworks incorporating skill decomposition, tool orchestration, andpersistent state tracking.
• Build and deploy supervised and unsupervisedML models for prediction, classification, anomaly detection, and patternrecognition tasks in production environments.
• Develop decision-making loops that balancetrade-offs between exploration vs. exploitation, cost vs. accuracy, and latencyvs. reasoning depth.
• Build structured memory systems includingepisodic memory stores, semantic memory layers, and vector-based memory withoptimised retrieval strategies.
• Design tool-calling architectures with strongexecution validation, retry mechanisms, and failure recovery strategies.
• Develop evaluation frameworks to measure agentand model performance using task success metrics, rollout simulations, modelaccuracy benchmarks, and multi-sample validation approaches.
• Integrate AI agents and ML models withenterprise systems.
• Deliver production-ready AI systems that meetoperational requirements around reliability, cost efficiency, throughput,observability, and enterprise security standards.

Requirements

• 3–10 years of experience building machinelearning or AI systems in production environments.
• Hands-on experience training, evaluating, anddeploying ML models using frameworks such as scikit-learn, XGBoost, or PyTorch— including feature engineering, cross-validation, and model monitoring inproduction.
• Strong experience building or extensivelycustomising agent frameworks for real-world applications.
• Hands-on experience designing tool-use orfunction-calling architectures under practical system constraints.
• Experience working with cloud-native AIplatforms, preferably GCP Vertex AI and Gemini, including model deployment,endpoint management, and AI pipeline orchestration.
• Experience integrating AI solutions withenterprise data systems — ERP APIs, data lakehouses (Databricks), or industrialdata sources (MES, IoT/sensor streams).
• Strong understanding of RAG architectures,vector databases, and retrieval strategies — with the ability to go beyondretrieval into agentic reasoning and action.
• Familiarity with real-time or streaming dataprocessing patterns (Pub/Sub, Kafka, or equivalent) for inference on liveoperational data.
• Strong Python engineering skills with a focuson scalable, reliable, and maintainable system design.

Candidates whose primary experience is limitedto RAG pipelines or prompt engineering without hands-on ML model development orproduction agent delivery may not be a strong fit for this role.

Nice to Have

• Experience with reinforcement learningtechniques such as policy gradients, value estimation, or reward modeling.
• Experience building multi-agent orcollaborative agent systems.
• Experience designing evaluation frameworks foragent robustness and reliability.
• Experience optimising LLM inference pipelinesfor latency, throughput, and cost efficiency.
• Familiarity with MLOps practices includingmodel versioning, drift monitoring, retraining pipelines, and model registries.
• Familiarity with distributed taskorchestration systems and large-scale AI workflow management.
• Prior experience in semiconductor,manufacturing, or industrial AI environments.