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Tier-1 brand and metro locations increase competition, but senior and specialized ML focus limits generalist applicants.
Highly specialized ML/LLM and financial services experience limits transferability across industries.
Explicit 10+ years, deep ML/LLM, cloud and production experience makes filters strict.
Lead design and development of AI/ML solutions using microservices, RESTful APIs, and distributed architectures in a large-scale financial services environment.
Oversee integration and deployment of AI foundational models, machine learning frameworks, and data engineering pipelines, ensuring end-to-end lifecycle management including production APIs and monitoring.
Drive cloud-native architecture adoption, enterprise-scale agile delivery (SAFe), and DevOps practices for improved scalability and observability.
10+ years of experience in microservices, distributed systems, and AI/ML development technologies (Python, PyTorch, TensorFlow, Java, Spring AI).
Proven expertise in data engineering (ETL/ELT, data storage, batch/stream processing) and big data technologies.
Hands-on experience with public cloud platforms (AWS, Azure, GCP) and container orchestration (Docker, CI/CD).
Experience in large-scale financial services enterprise environments.
Senior-level technologist with deep experience in AI/ML frameworks combined with cloud-native and data engineering skills tailored for financial services.
Strategic operator skilled in enterprise agile practices (SAFe) and managing end-to-end delivery of complex AI/ML and distributed system projects.
Experienced in stakeholder and executive communications, capable of translating technical solutions into business impact in a regulated enterprise context.