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, Adoption, 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:
- 4–8 years of experience in AI led 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 with proven hands on experience.
- Daily AI tool usage, demonstrable code contributions, and documented token usage.
- Strong analytical thinking and expertise in effectively utilizing data to derive AI solutions to solve business problems.
- Strong understanding of APIs, databases, cloud services, authentication, integrations, and deployment.
- Experience in Data and analytics platforms.
- Comfortable with structured and unstructured data.
- Experience with GenAI, LLMs, RAG, agents, AI workflow automation, prompt engineering, model integration and model training.
- 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.