About the role
- Translate research hypotheses and business requirements into focused, time-bound POC plans.
- Define the minimum prototype needed to test technical feasibility and potential business value.
- Design end-to-end architectures for applied AI, generative AI, and agentic solutions.
- Build working PoCs using LLMs, RAG, agents, machine learning, multimodal AI, knowledge graphs, APIs, and enterprise integrations.
- Compare models, frameworks, prompting strategies, retrieval methods, and architecture alternatives using measurable evidence.
- Build evaluation harnesses covering quality, groundedness, robustness, latency, cost, safety, and operational complexity.
- Integrate prototypes with representative enterprise data, applications, APIs, and workflows.
- Ensure that code, configurations, prompts, datasets, experiments, and evaluation results are documented and reproducible.
- Identify reusable components, technical patterns, and potential WRL accelerators.
- Produce clear technical handoffs for Development and Deployment teams.
- Coordinate contributions from AI, engineering, data, cloud, security, product, and domain specialists.
- Mentor AI engineers, POC engineers, and interns.
- Provide technical evidence for decisions to scale, revise, pivot, replicate, transfer, or stop a PoC.
- Proven experience designing and building end-to-end AI or machine-learning PoCs.
- Strong hands-on proficiency in Python and modern AI development frameworks.
- Experience with LLMs, RAG, agentic AI, APIs, data pipelines, and enterprise integrations.
- Ability to convert ambiguous research or business requirements into a working prototype and measurable evaluation plan.
- Strong understanding of machine learning, generative AI, model evaluation, and software-engineering fundamentals.
- Experience comparing models, technologies, platforms, and architectures using evidence.
- Practical knowledge of source control, testing, code review, containers, APIs, cloud platforms, and technical documentation.
- Experience working with researchers, software engineers, data engineers, cloud architects, security teams, and product teams.
- Strong communication skills with the ability to explain technical decisions, risks, costs, limitations, and trade-offs.
- Evidence of taking at least one AI initiative from concept through prototype and engineering or production handoff.
- Bachelor’s or master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Engineering, or a related field.
- Experience with OCI, Azure, AWS, or Google Cloud.
- Familiarity with MCP, multi-agent orchestration, vector databases, knowledge graphs, MLOps, LLMOps, and AI observability.
- Experience integrating AI with Oracle, SAP, Salesforce, or comparable enterprise platforms.
- Experience creating reusable AI accelerators, reference architectures, or internal platforms.
- Exposure to healthcare, life sciences, BFSI, or other regulated industries.
- Contributions to open-source projects, technical publications, patents, white papers, or developer communities.
- Experience mentoring engineers or leading small cross-functional technical teams.
- PoCs answer clearly defined business and technical questions.
- Prototype results are measurable, reproducible, and supported by evidence.
- Weak approaches are identified before significant production investment.
- Successful PoCs are transferred with clear architecture, code, evaluation results, risks, and implementation recommendations.
- Reusable components and patterns are identified across customer use cases.
- WRL develops a disciplined and repeatable POC capability.
- A hands-on builder who also thinks architecturally
- Comfortable working with ambiguity and incomplete information
- Disciplined about experimentation, evidence, and evaluation
- Able to distinguish a polished demonstration from a validated PoC
- Provider-agnostic and pragmatic about technology choices
- Conscious of quality, latency, cost, security, and maintainability
- Effective across research, engineering, business, and customer teams