Jobs at AuxoAI

Owns end-to-end architecture and technical outcomes for enterprise Gemini AI deployments on GCP. Designs the GenAI platform, agent ecosystem, context graph, data foundation, governance, runtime, and adoption strategy. Advises executives, leads architecture reviews, resolves cross-track tradeoffs, owns technical proposals and estimates, manages delivery risk and costs, and develops delivery teams. Requires production GenAI delivery, cloud architecture, graph and semantic data expertise, regulated-environment experience, and senior client engagement leadership.

Design, build, maintain, and optimize scalable AWS and Databricks data pipelines and ETL processes. Collaborate with data scientists and analysts, improve data storage performance and cost efficiency, implement governance and security practices, troubleshoot pipeline issues, and recommend emerging technologies. The role requires strong programming, SQL, database, data warehousing, and big data expertise, with containerization and orchestration experience preferred.

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.

Design, develop, and maintain scalable AWS-based data pipelines and ETL processes. Collaborate with data scientists and analysts to support analytics and machine learning initiatives. Optimize data storage, retrieval, performance, reliability, and cost. Implement governance and security best practices, troubleshoot pipeline issues, monitor systems, and recommend emerging technologies.

Lead the AWS practice’s technical strategy, architecture, delivery quality, customer engagements, and partnership initiatives. Design enterprise cloud, data, AI, and generative AI solutions; guide complex transformation programs; support executive briefings and presales; and establish governance and technical standards. Build reusable AWS solution assets, mentor architects and engineers, drive certifications, and help grow the consulting practice and revenue.

Design and deploy production-grade AI agents and machine learning systems for enterprise workflows. Responsibilities include building agent frameworks, tool-calling architectures, memory and retrieval systems, predictive models, evaluation frameworks, and integrations with enterprise data platforms. The role emphasizes reliable, observable, secure, and cost-efficient production systems using cloud-native AI platforms, real-time data processing, and advanced reasoning techniques.

Develop AI strategies aligned with client business objectives, assess opportunities for AI-driven value, and lead project execution. Partner with data scientists, engineers, business analysts, and product teams to deliver enterprise AI transformations. Engage clients as a trusted advisor, communicate complex AI concepts to nontechnical stakeholders, and contribute thought leadership on emerging AI technologies and best practices.

Design, build, and optimize scalable batch and streaming data pipelines across GCP, Databricks, and lakehouse environments. Develop ingestion, transformation, analytical, and ML-supporting data layers using Spark, Delta Lake, BigQuery, and SQL. Implement governance, quality controls, schema evolution, observability, CI/CD, and security practices. Collaborate with architects, AI engineers, and data scientists while mentoring junior engineers and contributing to technical design, documentation, code reviews, and agile delivery.

Designs and maintains scalable Microsoft Fabric data platforms, pipelines, Lakehouse architectures, and ETL/ELT workflows. Uses SQL, Python, and PySpark to ingest, transform, validate, standardize, deduplicate, and model enterprise data. Integrates Azure services, supports analytics and machine learning datasets, and implements monitoring, governance, lineage, security, CI/CD, and DataOps practices. The role also optimizes large-scale data processing and may support healthcare data, Power BI, streaming ingestion, and change-data capture.

Owns end-to-end architecture and business outcomes for enterprise Gemini agent ecosystems on Google Cloud. Designs the GenAI platform, agent landscape, context graph, data foundation, governance, runtime, and adoption strategy. Advises executives, leads architecture and security reviews, directs delivery teams, estimates proposals, manages delivery risk and costs, and enables client teams. Requires production GenAI delivery, enterprise graph and data-platform expertise, regulated-environment experience, and senior technical leadership across client engagements.

Build, evaluate, deploy, govern, and operate production LLM agents for client environments using Google Cloud’s Gemini Enterprise Agent Platform. Develop agent tools, MCP integrations, context graphs, retrieval pipelines, data systems, and multi-agent workflows. Pair with client engineers, support proofs of concept and adoption, instrument observability, publish agents, and ensure clean handover, production reliability, governance, and measurable business value.

Build scalable, high-performance frontend experiences for an AI platform. Responsibilities include architecting complex single-page applications, developing reusable components and libraries, collaborating with UX/UI designers, implementing frontend best practices, maintaining automated tests, and applying security and data protection standards. The role also involves cloud deployment, CDN usage, CI/CD, server-side rendering, and staying current with AI trends.

Owns the end-to-end health of a client’s enterprise data estate across legacy and modern platforms. Leads governance, SLAs, incident and problem management, data architecture standards, monitoring, data quality, and cross-party resolution. Establishes AIOps, intelligent observability, predictive failure detection, and self-healing automation while managing client stakeholders, demand governance, service reporting, and implementation roadmaps.

Owns the health and operational performance of an enterprise data platform across hybrid technical environments. Leads incident triage, problem management, governance, SLA reporting, data modeling standards, stakeholder escalations, and post-incident reviews. Builds AIOps and intelligent observability capabilities to automate detection, correlation, and resolution. Requires strong dbt, SQL, data warehousing, scheduling, ITSM, and data operations experience, with Snowflake and cloud cost optimization preferred.

Lead the strategy, development, delivery, and adoption of AI-powered marketing and sales products for enterprise clients. Translate business needs into product requirements, manage roadmaps, prioritize features, coordinate data science and engineering teams, oversee consulting engagements, and drive measurable business outcomes across revenue operations. The role also includes executive stakeholder management, client workshops, cross-functional delivery leadership, team mentoring, and change management.

Lead the design, training, evaluation, deployment, and scaling of production machine learning systems. Build LLM fine-tuning, RAG, agentic, real-time, and batch inference pipelines using modern MLOps practices. Apply transformers, diffusion models, RLHF, and multimodal architectures while integrating AI into business applications. Mentor junior engineers and promote scalable, tested, monitored, and responsible AI development.

Partners with business leaders, managers, and employees on engagement, performance management, talent development, retention, employee relations, and employee experience. Coaches managers, supports hiring and onboarding, tracks HR metrics, guides policy and employee lifecycle processes, handles concerns and conflicts, and supports organizational change and culture initiatives. The role also develops engagement action plans and provides HR insights in a fast-paced AI environment.

Build and deploy production LLM agents for client engagements on the Gemini Enterprise Agent Platform. Responsibilities include agent development, tool and MCP integration, graph-based grounding, data pipelines, evaluation, observability, governance, infrastructure deployment, and catalog publishing. The role partners closely with client engineers, supports proofs of concept and demos, and transfers maintainable solutions to client teams.

Lead AuxoAI’s US brand and ecosystem marketing engine across Google Cloud and AWS partnerships. Own partner co-marketing, field events, earned media, PR, executive speaker programs, thought leadership, and influencer engagement. Build pipeline and market credibility while managing the India-based content and design team, aligning creative output with US partner and field priorities. Track brand visibility, media impact, event ROI, partner marketing performance, and content quality.

Develop and execute AI strategies for clients, assess AI opportunities, lead projects end-to-end, collaborate cross-functionally, advise clients, and provide thought leadership on AI and analytics.