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Tier-1 firm, mid-level AI title, metro location, and broad cloud/ML skillset attract many qualified applicants.
Core ML, Python and cloud skills are transferable, but ESG/financial regulated context adds moderate industry specificity.
Explicit 2–6 years, plus mandatory Python, applied ML, cloud, and regulated-data experience increases screening rigor.
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Design, build, and own AI/ML data pipelines that process millions of financial and sustainability data points globally.
Develop and deploy cloud-native services on AWS, Azure, or GCP to deliver production-grade systems implementing complex financial screening methodologies.
Collaborate cross-functionally to maintain system observability, deployment pipelines, and ensure stability and accuracy in a regulated, client-facing environment.
2–6 years of hands-on software engineering experience with production system delivery.
Strong proficiency in Python and experience deploying AI/ML models in real-world contexts.
Hands-on cloud engineering experience with AWS, Azure, or GCP.
Work Experience Required: 2–6 years relevant software engineering; Notice Period: Not explicitly mentioned in the JD.
Experience with AI/ML frameworks such as PyTorch, TensorFlow, HuggingFace, or LangChain, indicating capability in applied AI.
Familiarity with large-scale distributed data pipelines and systems thinking oriented towards reliability and scalability.
Comfort working in ambiguous, evolving environments requiring engineering solutions for AI-driven workflows and regulated, client-impacting systems.