The Role
Design and deploy production-grade AI agents and predictive ML systems for enterprise workflows. Responsibilities include architecting agent frameworks, tool orchestration, memory systems, decision loops, evaluation frameworks, and integrations with enterprise data platforms. Build, deploy, monitor, and optimize ML models and agent systems for reliability, latency, cost, throughput, observability, and security. The role requires strong Python engineering, cloud AI platform experience, distributed systems knowledge, and production ML delivery.
Summary Generated by Built In
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
Location - Mumbai/Bangalore/Hyderabad/Gurgaon (Hybrid - 3 Days a week in Office)
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.
- Experience working with cloud-native infrastructure, APIs, containers, and distributed systems.
- Experience with production-grade logging, monitoring, metrics, alerting, and distributed system debugging.
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 Amazon Bedrock, SageMaker, or equivalent cloud AI services.
- Experience with Azure AI Foundry, Azure Machine Learning, Azure OpenAI, or other Azure AI services
- Experience with LangSmith, Langfuse, Grafana, Prometheus, or equivalent AI/LLM observability platforms.
- Experience with Kubernetes, Docker, Terraform, Helm, or other container orchestration and infrastructure-as-code technologies.
- Experience with GCP Vertex AI and Gemini ecosystem.
- 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.
Note: Given the urgency of the role, we are currently prioritizing candidates who can join immediately or within 2 weeks.
Skills Required
- 3-7 years of experience building machine learning or AI systems in production environments
- Hands-on experience training, evaluating, and deploying ML models using scikit-learn, XGBoost, PyTorch, or similar frameworks
- Experience with feature engineering, cross-validation, and production model monitoring
- Strong experience building or extensively customizing agent frameworks for real-world applications
- Experience designing tool-use or function-calling architectures under practical system constraints
- Experience with cloud-native AI platforms, preferably GCP Vertex AI and Gemini
- Experience integrating AI solutions with ERP APIs, Databricks, MES, IoT, or other enterprise data systems
- Strong understanding of RAG architectures, vector databases, retrieval strategies, agentic reasoning, and action
- Strong Python engineering skills focused on scalable, reliable, and maintainable system design
- Experience with cloud-native infrastructure, APIs, containers, and distributed systems
- Experience with production logging, monitoring, metrics, alerting, and distributed-system debugging
- Experience with Amazon Bedrock, SageMaker, or equivalent cloud AI services
- Experience with Azure AI Foundry, Azure Machine Learning, Azure OpenAI, or other Azure AI services
- Experience with LangSmith, Langfuse, Grafana, Prometheus, or equivalent AI observability platforms
- Experience with Kubernetes, Docker, Terraform, Helm, or similar infrastructure technologies
- Experience with GCP Vertex AI and Gemini ecosystem
- 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 optimizing 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
- Ability to join immediately or within two weeks
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The Company
What We Do
AuxoAI partners with enterprise leaders to build AI systems, enabling the creation of AI-first enterprises by moving from AI strategy to production-grade deployed systems in weeks.
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