AI Deployed Engineer

Key Responsibilities

1. Business Embedding and Outcome Ownership

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.

2. 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.

3. Rapid Prototyping and Value Validation

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

4. 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.

Required Skills and Experience

38 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 /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.