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Popular mid-level AI title, metro hiring, and known employer drive strong applicant competition.
Specialized ML/LLM production and fine-tuning skills are transferable but prefer ML-focused backgrounds.
Multiple explicit mandatory technical skills, LLM experience, cloud and years requirement increase filtering rigor.
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Design, develop, and optimize scalable AI-driven conversational systems improving intent recognition and response quality at enterprise scale.
Build and deploy real-time and asynchronous AI pipelines and backend microservices on cloud platforms to enhance system reliability and performance.
Collaborate cross-functionally to translate AI research into production-grade solutions, including fine-tuning large language models for efficient inference.
Bachelor's degree in Computer Science, Software Engineering, Data Science or related field with 3+ years experience; Master's/Ph.D. with 1–2 years also accepted.
Proficiency in Python with backend development experience.
Experience with AI/ML frameworks such as TensorFlow, PyTorch, Hugging Face, and fine-tuning large language models (e.g., vLLM).
Experience in API development, microservice architectures, and cloud platforms like AWS, GCP, or Azure.
Experience operating at the intersection of software engineering and applied machine learning in large-scale enterprise AI systems.
Demonstrated ability to improve conversational AI products focusing on scalability, latency, and reliability.
Proven track record working with modern LLM frameworks and deploying AI models in cloud-native microservice environments.