oracle
Sr Lead Software Engineer
JPMorgan Chase
Mumbai, IND
Full-time
Senior · 5+ years experience
Onsite
Discovered 3 days ago
OpenAI FrontierAnthropic Claude CoworkModel Context Protocol (MCP)LangChainLangGraphLlamaIndex
Free
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OpenAI FrontierAnthropic Claude CoworkModel Context Protocol (MCP)
Key Skills for This Role
OpenAI FrontierAnthropic Claude CoworkModel Context Protocol (MCP)LangChainLangGraphLlamaIndex
Full Job Posting
- Design and build autonomous agentic AI workflows capable of multi-step reasoning, tool use, and task execution with appropriate human-in-the-loop guardrails aligned with emerging enterprise patterns such as OpenAI Frontier and Anthropic Claude Cowork, and implement Model Context Protocol (MCP) integrations to enable AI agents to securely connect to and interact with external data sources, APIs, and enterprise tools in real time.
- Architect and implement prompt-based models on Large Language Models (LLMs) for NLP tasks tailored to financial services use cases such as intelligent search, conversational interfaces, and advisory support.
- Produce architecture and design artifacts for complex, distributed applications while ensuring design constraints, including latency, throughput, and regulatory requirements, are met and implement observability, monitoring, and feedback loops for agentic AI systems to track agent behavior, detect hallucinations, and ensure reliability in high-stakes financial applications
- Build and maintain scalable data pipelines and data processing workflows for both structured and unstructured data, leveraging cloud services to support LLM-based features and real-time client interactions ensuring seamless, low-latency experiences.
- Drive adoption and governance of approved AI-assisted engineering practices and Software Development Life Cycle toolchain across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
- Partner closely with product, design, and business stakeholders to translate client needs and business requirements into scalable AI-driven technical solutions.
- Ensure all client-facing solutions adhere to strict security, privacy, and regulatory standards applicable to the financial services industry, including data protection, model governance, and responsible AI practices, particularly as regulators increase scrutiny of autonomous AI agents in financial services.
- Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
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