Auxo AI

We are looking for a skilled DevOps Engineer with solid experience in cloud infrastructure management, CI/CD pipelines, and deployment on Kubernetes. The ideal candidate will have a strong background in Azure cloud platforms, with proficiency in configuring, automating, and optimizing cloud deployments to ensure scalability, reliability, and security of our systems.

Responsibilities:

Design, implement, and manage CI/CD pipelines using Azure DevOps, GitHub, and Jenkins for automated deployments of applications and infrastructure changes.

Architect and deploy solutions on Kubernetes clusters (EKS and AKS) to support containerized applications and microservices architecture.

Build and manage CI CD pipelines for multi region, multi environment production applications

Scalable Application deployments, optimal infra configuration

Collaborate with development teams to streamline code deployments, releases, and continuous integration processes across multiple environments.

Configure and manage Azure services including Azure Synapse Analytics, Azure Data Factory (ADF), Azure Data Lake Storage (ADLS), and other data services for efficient data processing and analytics workflows.

Utilize AWS services such as Amazon EMR, Amazon Redshift, Amazon S3, Amazon Aurora, IAM policies, and Azure Monitor for data management, warehousing, and governance.

Implement infrastructure as code (IaC) using tools like Terraform or CloudFormation to automate provisioning and management of cloud resources.

Ensure high availability, performance monitoring, and disaster recovery strategies for cloud-based applications and services.

Develop and enforce security best practices and compliance policies, including IAM policies, encryption, and access controls across Azure environments.

Collaborate with cross-functional teams to troubleshoot production issues, conduct root cause analysis, and implement solutions to prevent recurrence.

Stay current with industry trends, best practices, and evolving technologies in cloud computing, DevOps, and container orchestration.

Requirements:

Bachelor’s degree in Computer Science, Engineering, or related field; or equivalent work experience.

5+ years of experience as a DevOps Engineer or similar role with hands-on expertise in AWS and Azure cloud environments.

Strong proficiency in Azure DevOps, Git, GitHub, Jenkins, and CI/CD pipeline automation.

Experience deploying and managing Kubernetes clusters (EKS, AKS) and container orchestration platforms with Helm & ArgoCD.

Deep understanding of cloud-native architectures, microservices, and serverless computing.

Familiarity with Azure Synapse, ADF, ADLS, and AWS data services (EMR, Redshift, Glue) for data integration and analytics.

Solid grasp of infrastructure as code (IaC) tools like Terraform, CloudFormation, or ARM templates.

Experience with monitoring tools (e.g., Prometheus, Grafana) and logging solutions for cloud-based applications.

Excellent troubleshooting skills and ability to resolve complex technical issues in production environments.

AuxoAI is seeking a techno-functional Developer with strong expertise in enterprise platforms such as NetSuite and/or HubSpot, combined with hands-on experience in AI engineering. The ideal candidate will bridge business processes and technical implementation, leveraging domain knowledge and AI capabilities to design intelligent, scalable solutions. 

This role offers an exciting opportunity to work at the intersection of business systems and AI building automation, insights, and intelligent workflows that enhance client operations and decision-making. 

Additionally, you are required to stay up to date on AI trends, enterprise platform innovations, and best practices, contributing to continuous learning and capability building within the team. 

Location - Mumbai/Bangalore/Hyderabad/Gurgaon (Hybrid - 3 Days a week in Office)

Responsibilities: 

• Design and implement AI-powered solutions integrated with NetSuite and/or HubSpot to optimize business workflows and decision-making. 

• Collaborate with business stakeholders to understand functional requirements across domains such as CRM, ERP, sales, marketing, and finance. 

• Develop and customize NetSuite (SuiteScript, SuiteTalk) and/or HubSpot (Workflows, APIs, Custom Objects) solutions aligned with business needs. 

• Build intelligent automation using AI/ML models, LLMs, and rule-based systems to enhance platform capabilities. 

• Integrate enterprise systems with data platforms and AI services to enable end-to-end data-driven workflows. 

• Translate business processes into scalable technical architectures and reusable components. 

• Ensure data quality, governance, and security across integrated systems. 

• Troubleshoot and optimize system performance, integrations, and AI pipelines. 

• Act as a techno-functional advisor to clients, identifying opportunities for AI-driven transformation and platform optimization. 

• Contribute to solution accelerators, reusable frameworks, and internal knowledge sharing. 

Requirements:

• Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or a related field. 

• 3–7 years of experience in a techno-functional role involving NetSuite and/or HubSpot implementations. 

• Strong domain knowledge in CRM (HubSpot) and/or ERP (NetSuite) processes such as sales, marketing automation, finance, and operations. 

• Hands-on experience with platform customization: 

NetSuite (SuiteScript, SuiteFlow, SuiteTalk APIs)  

HubSpot (HubL, Workflows, APIs, CRM customization) 

• Experience in AI , including working with LLMs, prompt engineering and AI application development. 

• Strong programming skills in Python (preferred). 

• Experience with REST APIs, system integrations, and middleware platforms. 

• Familiarity with cloud platforms (AWS preferred) and data pipelines is a plus. 

• Understanding of data models, ETL processes, and analytics workflows. 

• Strong problem-solving skills with the ability to bridge business and technical perspectives. 

• Excellent communication and stakeholder management skills.  

AuxoAI is seeking a skilled and experienced Senior AI Engineers to join our dynamic team. The ideal candidate will have 5+ years of prior experience in software engineering. This role involves collaborating with cross-functional teams to drive innovation and deliver impactful AI-driven products. This role is responsible for implementing our strategic direction on AI, intelligent automation, and data-powered operations. This role will also guide the implementation of AI solutions across various projects, with an eye on AI governance.

Location - Mumbai/Bangalore/Hyderabad/Gurgaon (Hybrid - 3 Days a week in Office)

Responsibilities:

· AI/ML Solution Development: Design, develop, and deploy AI/ML technology stacks from concept to production and deployment

· Technical Leadership: Provide technical leadership and mentorship to junior engineers, guiding them in best practices and advanced techniques.

· Develop and optimize Generative AI workflows, including prompt engineering, fine-tuning, RAG and LLM-based applications.

· Work with Large Language Models (LLMs) such as Claude, Llama, Mistral, and GPT, ensuring efficient adaptation for various use cases.

· Design and implement AI-driven automation using agentic AI systems and orchestration frameworks like Autogen, LangGraph, and CrewAI.

· Leverage cloud AI infrastructure (AWS, Azure, GCP) for scalable deployment and performance tuning.

· Collaborate with cross-functional teams to deliver AI-driven solutions.

· Front ending customer discussions, customer engagement and success stories

· Collaborate with stakeholders to gather requirements and translate them into technical specifications

Requirements

Bachelor’s in computer science, Engineering, or a related field

· Overall 5+ year’s experience in software engineering and 2+ years of experience in AI/ML, with expertise in Generative AI and LLMs. 

· Experience with AWS Bedrock or Azure OpenAI studio or similar enterprise AI environments

· Strong proficiency in Python and experience with AI/ML frameworks like PyTorch and TensorFlow

· Experience with containerization (e.g., Docker, Kubernetes), version control systems (e.g., Git) and software development methodologies (e.g., Agile, Scrum)

· Knowledge of advanced prompt engineering techniques

· Experience in AI workflow automation and model orchestration

· Hands-on experience with API development using Flask or Django

Note: Given the urgency of the role, we are currently prioritizing candidates who can join immediately or within 2-3 weeks.

Role Summary:

AuxoAI is seeking a skilled and experienced Data Engineer to join our dynamic team. The ideal candidate will have 3-8 years of prior experience in data engineering, with a strong background in working on modern data platforms. This role offers an exciting opportunity to work on diverse projects, collaborating with cross-functional teams to design, build, and optimize data pipelines and infrastructure.

Responsibilities:

• Design, develop, and maintain data pipelines using Databricks (PySpark / Spark SQL)

• Build and manage data pipelines across Bronze, Silver, and Gold layers using Delta Lake

• Implement ETL/ELT workflows for batch and near real-time processing

• Work with Databricks Workflows for orchestration and job scheduling

• Leverage Unity Catalog for data governance, access control, and metadata management

• Optimize Spark jobs, cluster configurations, and cost efficiency

• Collaborate with business and analytics teams to translate requirements into scalable data models

• Integrate data from multiple sources (APIs, databases, cloud storage)

• Ensure data quality, validation, and observability across pipelines

• Troubleshoot and debug data pipeline issues, providing timely resolution and proactive monitoring

Qualifications:

• Bachelor’s degree in computer science, Engineering, or a related field.

• Overall 3+ years of prior experience in data engineering, with a focus on designing and building data pipelines

• Hands-on experience with Databricks platform and ecosystem

• Strong proficiency in Python (PySpark) and SQL

• Experience working with Delta Lake (ACID transactions, time travel, schema evolution)

• Good understanding of data warehousing concepts and dimensional modeling

• Familiarity with Unity Catalog (data governance, RBAC, lineage basics)

• Understanding of Spark performance tuning and optimization techniques

• Experience with cloud platforms (AWS / Azure / GCP)

• Working knowledge of Git and CI/CD practices

• Familiarity with implementing CI/CD processes or other orchestration tools is a plus.

AuxoAI is seeking a Senior Applied Scientist to design and deploy structured knowledge systems that enable reliable, schema-grounded AI and agent reasoning.

This role sits at the intersection of large language models, knowledge graphs, semantic architectures, and hybrid retrieval systems. The ideal candidate will build systems that transform unstructured data into structured knowledge representations, enforce semantic constraints, and enable hybrid symbolic–neural reasoning in production environments.

You will play a key role in designing scalable semantic infrastructures that support advanced AI use cases such as GraphRAG pipelines, structured extraction, and agent reasoning workflows.

You will work on problems where existing architectures may not be sufficient and will experiment with new approaches that combine machine learning, knowledge graphs, semantic constraints, and classical AI techniques to build reliable, production-grade systems.

Location - Mumbai/Bangalore/Hyderabad/Gurgaon (Hybrid - 3 Days a week in Office)

Responsibilities:

• Design schema-guided information extraction systems using zero-shot and few-shot structured prompting, constrained decoding approaches such as JSON schema enforcement or grammar-based decoding, and function-calling or tool-driven extraction techniques.
• Develop recursive or multi-stage extraction pipelines capable of handling nested entities, hierarchical structures, and cross-document relationships.
• Build ontology-driven systems using frameworks such as LinkML, OWL, SHACL, or similar schema modeling tools, and implement knowledge representations using RDF triples or labeled property graphs.
• Design and optimize entity resolution algorithms using techniques such as blocking strategies, embedding similarity, and rule-based matching.
• Develop ontology alignment techniques and graph embedding models such as Node2Vec or TransE-style approaches where appropriate.
• Design hybrid retrieval architectures combining dense vector retrieval, sparse retrieval techniques, and graph traversal algorithms such as BFS, DFS, path ranking, and neighborhood expansion.
• Build validation systems that enforce schema conformance, detect semantic inconsistencies, and reduce hallucinated or invalid structured outputs.
• Integrate structured knowledge systems into GraphRAG pipelines, agent planning frameworks, and tool-selection workflows.
• Deliver production-grade semantic systems with clear targets for latency, scalability, reliability, and data integrity.

Requirements

• 5+ years of experience building production AI or machine learning systems.
• Strong experience designing and implementing knowledge graphs or ontology-driven architectures.
• Hands-on experience implementing structured extraction techniques, including grammar-constrained decoding, JSON schema enforcement, or AST-style parsing approaches.
• Experience building entity resolution systems beyond simple embedding similarity methods.
• Experience working with graph query languages such as SPARQL or Cypher and optimizing graph query performance.
• Familiarity with RDF, OWL, or property graph data models and semantic data architectures.
• Strong Python engineering skills, with emphasis on data validation, schema integrity, and system reliability.
• Experience designing hybrid symbolic and neural AI systems.

Nice to Have:

• Experience implementing graph algorithms such as PageRank, community detection, or shortest-path algorithms for reasoning chains.
• Experience building graph-enhanced retrieval systems such as GraphRAG.
• Experience designing compositional semantic extraction pipelines.
• Experience implementing reasoning engines or rule-based inference systems.
• Experience benchmarking and evaluating structural extraction accuracy and consistency.

AuxoAI is hiring a Senior Applied Scientist to design and deploy production-grade AI agents capable of structured reasoning, planning, and decision-making.

This role focuses on building intelligent agent systems that combine LLM-based reasoning with classical planning, search algorithms, and optimization techniques. The ideal candidate will develop robust agent architectures that operate reliably in real-world environments with constraints around latency, cost, uncertainty, and limited context windows.

You will work on advanced AI systems that power autonomous workflows, decision engines, and tool-driven agent ecosystems.

You will work on problems where existing architectures may not be sufficient, and will be expected to experiment with new approaches that combine machine learning, graph algorithms, and classical AI techniques 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.
• Implement planning and search algorithms such as Monte Carlo Tree Search (MCTS), beam search, A search, heuristic search, and graph-based planning approaches* to support complex decision-making tasks.
• 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 optimized retrieval strategies.
• Design tool-calling architectures with strong execution validation, retry mechanisms, and failure recovery strategies.
• Develop evaluation frameworks to measure agent performance using task success metrics, rollout simulations, and multi-sample validation approaches.
• Improve agent performance through techniques such as distillation, synthetic trajectory generation, prompt compression, and context pruning.
• Deliver production-ready agent systems that meet operational requirements around reliability, cost efficiency, throughput, and observability.

Requirements

• 2-5 years of experience building machine learning or AI systems in production environments.
• Strong experience implementing search or planning algorithms beyond basic use cases, including tree search or heuristic-based planning approaches.
• Hands-on experience with Monte Carlo Tree Search (MCTS) or related decision-making frameworks.
• Strong understanding of state-space representations, heuristic design, and decision boundary trade-offs.
• 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.
• Strong Python engineering skills with a focus on scalable and reliable 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 optimizing LLM inference pipelines for latency, throughput, and cost efficiency.
• Familiarity with distributed task orchestration systems and large-scale AI workflow management.