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Mid-level, metro-based GenAI role with broad full-stack and ML requirements attracts many qualified applicants.
ML productionization and LLM expertise favors ML-engineering backgrounds but remains moderately transferable across industries.
Explicit 5+ years, 3+ ML years, and specific ML, infra and frontend stack requirements.
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Develop end-to-end GenAI features including backend APIs, model integration, deployments, and monitoring for business planning applications.
Optimize and integrate large language models (LLMs) for specific business use cases including prompt engineering and retrieval-augmented generation (RAG).
Build conversational interfaces and agentic workflows to make complex planning tasks accessible via natural language, while implementing evaluation frameworks for feature quality.
Minimum 5 years software engineering experience with at least 3 years focused on ML/AI systems.
Strong Python programming with experience in ML frameworks like PyTorch, TensorFlow, and Transformers.
Experience building and deploying production LLM-powered applications with prompt engineering and RAG knowledge.
Bachelor's degree in computer science, machine learning, or related field.
Experienced in full-stack GenAI development combining deep ML knowledge with strong software engineering skills, including backend and frontend (React/TypeScript).
Familiar with cloud infrastructure, microservices, containerization (Docker/Kubernetes), CI/CD pipelines, and model serving frameworks.
Able to collaborate with data scientists and product teams to productionize ML models and deliver user-facing AI features impacting enterprise planning workflows.