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Mid-level ML role, metro locations, broad skillset and popular title increase applicant competition.
Core ML/MLOps skills are transferable, but consulting and sustainability domain preferences moderately limit portability.
Explicit 6–8 years, leadership requirement, consulting experience, and technical MLOps/LLM mandates raise strictness.
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Lead design, development, and deployment of AI/ML solutions focused on sustainability, ESG, and workplace safety domains.
Architect scalable, robust LLM- and BERT-based applications and AI agents using Python and relevant frameworks, including LangGraph and LangChain.
Manage end-to-end AI/ML lifecycle from scoping to production deployment, acting as client advisor and mentoring junior team members.
6–8 years of AI/ML project delivery experience, including 2–3 years leading teams or solution architecture.
Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML, Engineering, or related fields; PhD is a plus.
Proven experience in consulting or client-facing roles with executive stakeholder communication.
Expertise with AI/ML frameworks (TensorFlow, PyTorch), generative AI/LLM engineering, Microsoft Azure AI ecosystem, and production MLOps deployment.
Experienced in sustainability or health & safety data ecosystems, with understanding of ESG frameworks and reporting standards like GRI, SASB, ESRS.
Strong strategic orientation combining AI/ML technical leadership with business acumen to align technology solutions with client needs.
Skilled in architecting enterprise-grade AI solutions and driving innovation in generative AI and responsible AI principles within consulting or advisory environments.