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Mid-level generalist AI role, broad skillset requirements and metro/hybrid setup increase applicant competition.
Core ML/AI skills transfer across industries, though recruitment domain knowledge is beneficial.
Explicit years, required ML/LLM skills, cloud experience and production deployment expectations impose strict filters.
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Lead design, development, and deployment of scalable AI/ML models including LLMs, RAG, NLP, and generative AI applications across enterprise products and automation.
Provide technical direction, architectural guidance, and enforce quality standards for AI solutions ensuring scalability, security, compliance, and alignment to business goals.
Collaborate cross-functionally with Product, Innovation, Technology, and Data Engineering teams to execute AI strategy and support organization-wide AI adoption and AI-driven workflow automation.
Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, or related field required; Master’s preferred.
3–5+ years of hands-on experience developing and deploying ML/AI models in production, including LLMs, NLP, generative AI, and API integrations.
Proficiency in Python and ML frameworks (TensorFlow, PyTorch, Hugging Face, scikit-learn); experience with cloud AI platforms (AWS SageMaker, Azure ML, GCP Vertex AI).
Experience with prompt engineering, RAG pipelines, embeddings, vector databases, and strong SQL skills; familiarity with MLOps preferred.
Experienced in leading AI/ML projects in enterprise environments with a focus on scalable, secure AI system architecture and full model lifecycle management.
Strong technical leadership ability to enforce development standards and translate business requirements into AI solutions with practical deployment experience.
Skilled at collaborating across multiple departments to align AI projects with strategic business objectives and drive innovation in AI adoption.