Forward Deployed AI Engineer

2 months ago

Remote, IndiaMid Level / Senior

Responsibilities

Partner with business stakeholders to identify opportunities and translate operational requirements into AI-powered solutions.
Design, build, deploy, and hand over AI workflows and automation using Azure AI, Copilot Studio, and Microsoft Fabric.
Integrate AI solutions with SAP, Salesforce, and other enterprise business systems.
Own the full delivery lifecycle from discovery and solution design through production deployment and operational handover.
Implement DevOps, MLOps, AIOps, CI/CD, model versioning, automated testing, monitoring, and safe fallback practices.
Provide technical leadership and guidance to engineers, analysts, and project teams without formal line management authority.
Develop prototypes and proof-of-concepts, establish engineering standards and reusable patterns, and evaluate new technologies.
Ensure solutions satisfy security, compliance, governance, and AI Landing Zone requirements.
Drive adoption outcomes, continuous improvement, and measurable reductions in manual effort, cycle time, or operational cost.

4–5+ years of experience in software engineering, AI/ML workflow delivery, or systems integration with production AI delivery experience.
At least 2 years operating in a senior or lead technical capacity, making architectural decisions and guiding engineers.
Hands-on production experience with DevOps, MLOps, and AIOps, including CI/CD pipeline ownership, model versioning, automated testing, and live monitoring.
Strong proficiency in Python and SQL, with experience integrating enterprise systems through APIs and event-driven patterns.
Production experience with Azure AI Foundry, Microsoft Copilot Studio, and Microsoft Fabric.
Experience delivering across the full lifecycle from discovery and design through deployment, business handover, and adoption.
Experience working directly with business stakeholders and owning delivery and adoption outcomes.
Desirable experience with SAP Joule, agentic AI integrations, AI Landing Zone governance, Docker, Kubernetes, and Azure cloud-native deployment.
Understanding of multi-agent coordination patterns using Copilot Studio or Azure AI Foundry.
Knowledge of healthcare data privacy requirements including GDPR or HIPAA.
Bachelor’s or Master’s degree in Computer Science, Software Engineering, Information Systems, or a related field, or equivalent practical experience.
Azure or MLOps/AIOps certifications, such as Azure Developer Associate, Azure Data Engineer Associate, or Azure DevOps Engineer Expert, are desirable.

Hybrid work arrangement, indicated by the #LI-Hybrid designation.
Opportunity to deliver AI solutions with measurable business and operational impact at a global medical products and technologies company.

5,001-10,000 employees