Source description
About the role
- Orchestrate endtoend data journeys within the ecosystem of unstructured financial data to generate actionable reporting insights. - Analyze existing processes across client mandates and translate them into detailed process flowcharts. - Understand business requirements and develop solutions aligned with industry best practices. - Design data pipelines and engineering infrastructure to support scalable machine learning systems. - Identify, assess, and integrate new technologies to enhance the performance, maintainability, and reliability of ML systems. - Facilitate the development and deployment of proofofconcept AI solutions. - Collaborate with Product Managers, software engineers, and crossfunctional stakeholders to ensure effective deployment and operationalization of ML models. - Stay current with industry trends and advancements in MLOps. - Ensure all AI applications comply with data privacy, security, and ethical standards. - Serve as a subjectmatter expert on large language models and natural language processing for largescale unstructured data. - Design and optimize agentic AI systems that dynamically adapt to platform needs and enhance enduser interactions. - Identify highimpact AI use cases and integrate both offtheshelf and custombuilt solutions to address business needs. Requirements - Bachelors or Masters degree in Computer Science, Information Technology, or a related field. - Strong foundation in AI concepts, including LLMs, RAG architectures, embedding, vector databases, prompt engineering, agent workflows, and guardrail design. - Handson prototyping capability: building POCs, calling APIs, using basic Python/JavaScript for quick validation, parsing JSON, and running feasibility checks. - Expertise in workflow and agent design, including intent mapping, tool/actions definition, escalation paths, and multistep orchestration. - Working knowledge of system integration fundamentals: reading API documentation, understanding events and webhooks, managing data flows, and handling errors. - Ability to define evaluation frameworks and metrics: KPIs, prompt testing, A/B experimentation, performance measurement, and cost analysis. - Familiarity with security and reliability practices including PII handling, access control, logging, SLAs, and system monitoring. - Strong documentation and communication skills: writing transparent requirements, process workflows, integration guides, and delivering concise updates. - Productdriven mindset: mapping business processes, gathering user needs, defining MVPs, and prioritizing roadmaps. - Ability to collaborate effectively with engineering, AI/ML teams, QA, operations, and business stakeholders. - Knowledge of governance and continuous improvement practices: version control, change management, reusable templates, and optimization cycles. .
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