Job Summary:
We are looking for a seasoned Generative AI (GenAI) Solution Architect with a strong background in Financial Services (FS) to lead the design and implementation of cutting-edge AI-powered solutions. This is a strategic role that requires deep technical expertise across AI/ML architecture, DevSecOps practices, and secure, scalable cloud-native systems.
You will partner with cross-functional teams, including data science, platform engineering, DevOps, and cybersecurity, to architect GenAI solutions that are secure, scalable, and compliant with enterprise-grade controls. A strong understanding of AI use cases in FS, especially in areas such as risk modeling, fraud detection, personalization, and document summarization, is highly preferred.
Key Responsibilities:
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Architect and lead GenAI solution development including prompt engineering, model fine-tuning, RAG (Retrieval-Augmented Generation) design, and API deployment.
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Define CI/CD control gates to ensure model and code deployment follow secure MLOps principles.
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Lead vulnerability detection and remediation planning for AI workflows and APIs, incorporating enterprise security standards.
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Design and oversee implementation of containerized or serverless solutions using platforms such as AWS Lambda, Azure Functions, Docker, or Kubernetes.
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Work closely with cybersecurity teams to incorporate OWASP-based threat modeling, including AI-specific vulnerabilities (e.g., prompt injection, model poisoning).
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Serve as the technical authority in evaluating GenAI tools, LLM platforms (OpenAI, Azure OpenAI, Anthropic, etc.), vector databases, and orchestration frameworks like LangChain or LlamaIndex.
Must-Have Skills:
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10+ years of overall software architecture experience, with at least 2–3 years in AI/ML and recent hands-on GenAI work.
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Strong knowledge of cloud-native development, especially AWS or Azure (certifications preferred).
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Proven experience implementing MLOps pipelines with security checkpoints and rollback mechanisms.
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Familiarity with AI-specific OWASP risks and mitigation practices.
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Deep understanding of containers and serverless computing, including secure deployment and runtime monitoring.
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Demonstrated success in enterprise-scale solution architecture, especially in Financial Services, Banking, or Insurance domains.
Preferred Qualifications:
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Experience with RAG-based GenAI implementations, LLM hosting, and fine-tuning.
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Knowledge of data governance, bias mitigation, and compliance in AI solutions.
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Experience in using vector DBs like Pinecone, FAISS, Weaviate, or Qdrant.
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Exposure to enterprise security frameworks such as NIST, ISO 27001.