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Tier-1 brand plus metro location raise applicant competition, but senior specialization moderates it.
Highly specialized ML/AI, prompt/foundation model and financial domain experience require domain-specific backgrounds.
Explicit 10+ years, deep ML, data engineering, and cloud requirements create strict screening filters.
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Lead development and deployment of AI/ML solutions using microservices, REST APIs, and distributed architectures.
Drive implementation of foundational AI models and machine learning frameworks like PyTorch, TensorFlow, and prompt engineering for generative AI optimization.
Oversee data engineering pipelines, cloud-native architectures on AWS/Azure/GCP, and large-scale agile delivery in financial services contexts.
10+ years of experience in microservices, RESTful APIs, distributed systems, and AI/ML technologies.
Proficiency with Python, FastAPI, Java, Spring AI, PyTorch, TensorFlow, and foundational AI models (e.g., Gemini, OpenAI).
Experience with cloud platforms AWS, Azure, or GCP and data engineering including ETL/ELT, batch/stream processing, and big data.
Familiarity with SAFe Agile frameworks and enterprise-scale agile delivery in financial services environments.
Senior-level technologist comfortable operating across AI/ML development, data engineering, and cloud architecture at scale.
Experienced in managing complex, multi-technology stacks in regulated, large financial enterprise environments.
Able to engage with stakeholders and report to executives effectively, integrating agile and DevOps practices.