AI Deployed Engineer

• Embed with business and engineering teams to own AI outcomes within a defined business domain.
• Build and deliver AI solutions hands-on; this is an execution role, not an advisory role.
• Convert AI potential into production value through code-first delivery and active repository contributions.

1. Problem Discovery and Solution Design

• Understand business processes, pain points, systems, data flows, and success metrics.
• Translate problems into MVPs, integrations, automations, and production-ready solutions with an ownership mindset
• Build across APIs, databases, cloud platforms, workflow tools, enterprise systems, and AI/GenAI technologies.

1. Rapid Prototyping and Value Validation

• Own the journey from discovery to working solution, rapidly proving business value through pilots and POCs

1. Integration, AdEligible to workion, and Scale

• Integrate with enterprise platforms, data systems, workflows, collaboration tools, and third-party APIs.
• Document architectures, implementation playbooks, reusable components, and customer-specific solution guides.
• Feed field learnings into product roadmap, accelerators, and go-to-market propositions.

Artificial Intelligence (AI), Artificial Intelligence (AI), Business Processes, Metrics, Cloud Computing, Application Programming Interface (API), Rapid Prototyping, Product Planning, Customer Relations, Consulting, * 3-8 years of experience in engineering, implementation, product, consulting, or customer-facing technology roles.

• Strong engineering fundamentals with hands-on coding experience in Python, JavaScript/TypeScript, Java, .NET/C#, or Go.
• AI proficiency is mandatory; candidates may come from software engineering, data science, UX, or related domains.
• Daily AI tool usage, demonstrable code contributions, and documented token usage.
• Strong understanding of APIs, databases, cloud services, authentication, integrations, and deployment.
• Comfortable with structured and unstructured data.
• Experience with GenAI, LLMs, RAG, agents, AI workflow automation, prompt engineering, or model integration.
• Cloud experience across AWS, Azure, or Google Cloud.
• Good communication, adaptability, and problem-solving in ambiguous environments.

Good to Have

• Knowledge of ML algorithms, model building, deployment, deep learning, and NLP.
• Experience integrating with Salesforce, Jira, Rally, Oracle, ServiceNow, Microsoft Dynamics or similar platforms.
• Familiarity with data engineering, ETL/ELT pipelines, BI dashboards, analytics, and reporting workflows.
• Healthcare exposure, especially contact centers, claims automation, finance, or technology services.

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