About this role
Summary: Serves as the embedded GenAI expert for a squad of software engineers, translating architecture decisions into production GenAI implementations. Trains and mentors team members while independently driving complex GenAI and agentic AI features.
Job Summary
We are seeking a highly skilled Generative AI Developer to join the AI Store product team as an embedded AI expert within a squad of 7–10 traditional software engineers (backend, frontend, and full-stack developers). This is not a conventional developer role — the ideal candidate will serve as the squad's single point of contact for all things AI and Generative AI: hands-on developer, internal trainer, technical guide, and implementation lead.
Working under the guidance of the Generative AI Architect, the developer will translate architectural decisions into production-grade implementations, uplift team capabilities through training and mentorship, and independently drive the development of complex GenAI and Agentic AI features for enterprise-grade solutions.
Key Responsibilities
- Act as the squad's embedded GenAI expert and single point of contact for all AI/GenAI-related decisions, implementation, and guidance.
- Design, develop, and optimize production-grade GenAI and Agentic AI applications, services, and pipelines in Python.
- Work under the GenAI Architect to interpret architectural blueprints and implement complex GenAI components, ensuring alignment with enterprise standards.
- Integrate Large Language Models (LLMs) — including OpenAI, Azure OpenAI, Hugging Face, Anthropic, and Cohere — into enterprise workflows and products.
- Design and implement Retrieval-Augmented Generation (RAG) pipelines, multi-agent orchestration systems, and Agentic AI flows.
- Build, maintain, and evolve APIs, automation scripts, and AI pipelines on the AIForce platform.
- Train and mentor squad members (backend, frontend, full-stack developers) on GenAI concepts, tools, frameworks, and best practices — enabling the broader team to actively contribute to AI feature development.
- Conduct LLM performance evaluation, prompt optimization, and model fine-tuning as required.
- Champion Safe AI, AI governance, and responsible AI development practices across the squad.
- Monitor, test, and troubleshoot deployed GenAI models and services in production environments.
- Stay current with emerging GenAI frameworks, LLM advances, and industry trends; proactively assess and introduce relevant innovations.
Skill Requirements
- Strong proficiency in Python (3+ years), including experience building production-grade applications.
- Proven hands-on experience with Agentic AI and GenAI frameworks: LangChain, LlamaIndex, Hugging Face Transformers, AutoGen, CrewAI, or similar.
- Demonstrated experience designing and implementing RAG architectures, vector search pipelines, and multi-agent systems.
- Familiarity with LLM APIs: OpenAI, Azure OpenAI, Anthropic, Cohere, and open-source models.
- Experience with vector databases (e.g., Pinecone, Weaviate, Azure AI Search, FAISS, Chroma).
- Strong knowledge of prompt engineering, chain-of-thought techniques, and LLM evaluation/observability methods.
- Understanding of LLM fine-tuning approaches (RLHF, PEFT, LoRA) — practical experience will be added advantage.
- Knowledge of Safe AI principles, AI security, AI governance frameworks, and responsible AI development practices.
- Hands-on experience with developing AI solutions on any cloud platform
- Solid software engineering fundamentals: Git, CI/CD pipelines, automated testing, API design.
- Effective communication and mentoring skills — able to explain complex AI concepts to non-AI developers clearly.
Other Requirements
- Experience with MLOps tooling (MLflow, Azure ML Pipelines, or equivalent).
- Exposure to multimodal models (vision-language models, speech-to-text integration).
- Prior experience working as an AI champion or tech lead within a cross-functional product team.
- Familiarity with enterprise AI governance and compliance frameworks.