21 Aug
|
Nameless
|
Gurugram
- Orchestrate end‑to‑end 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 recent technologies to enhance the performance, maintainability, and reliability of ML systems.
- Facilitate the development and deployment of proof‑of‑concept AI solutions.
- Collaborate with Product Managers, software engineers, and cross‑functional 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 subject‑matter expert on large language models and natural language processing for large‑scale unstructured data.
- Design and optimize agentic AI systems that dynamically adapt to platform needs and enhance end‑user interactions.
- Identify high‑impact AI use cases and integrate both off‑the‑shelf and custom‑built solutions to address business needs.
Requirements
- Bachelor’s or Master’s 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.
- Hands‑on 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 multi‑step 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 clear requirements, process workflows, integration guides, and delivering concise updates.
- Product‑driven 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.