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Niche LLM and MLOps requirements reduce applicant density despite known employer and metro location.
ML/LLM and MLOps skills transfer across industries, but product engineering context favors domain-aligned candidates.
Numerous mandatory LLM, MLOps, cloud, and fullstack skills plus seniority indicate strict filtering.
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Design, develop, test, and deploy AI-powered engineering software applications focusing on automation, productivity, and digital transformation.
Develop APIs, microservices, and cloud-native applications using modern programming languages and frameworks.
Collaborate with architects, product owners, and global teams in Agile/Scrum processes to ensure system reliability, quality, and performance.
Experience with Generative AI, LLMs, LangChain, RAG, or AI agents.
Proficiency in Python, React, Node.js, FastAPI.
Experience with containerization (Docker/Kubernetes), DevOps, MLOps, and cloud-native architectures.
Experience Required: Not explicitly mentioned in the JD.
Engineer with strong background in AI/ML integrations and automation within scalable software products.
Experienced in cross-functional collaboration following Agile/Scrum methodologies, working with global teams.
Skilled in designing cloud-native, containerized applications emphasizing engineering productivity and SDLC automation.