AI Solutions Engineer

Role Overview

You will be a hands-on AI Solutions Engineer responsible for designing, building, and deploying AI-powered tools that integrate with enterprise data platforms and APIs. The role combines deep technical implementation skills with strong business partnership, working directly with investment, risk, and analytics users to deliver AI solutions embedded into real workflows.

Key Responsibilities

• Build AI Tools Integrated with Data & APIs
• Design and implement AI-powered applications that integrate with enterprise data platforms, APIs, and services.
• Build copilots, assistants, and AI workflows that query, interpret, and act on structured and unstructured data.
• Integrate AI tools with internal systems, vendor APIs, and curated data layers.
• HandsOn GenAI & LLM Development
• Implement solutions using large language models (LLMs) and modern GenAI frameworks.
• Build RetrievalAugmented Generation (RAG) patterns using enterprise data sources.
• Design prompt strategies, orchestration logic, and guardrails for reliable AI behavior.
• Develop agent-style workflows that combine reasoning, tool usage, and data access.
• Business Liaison & Solution Design
• Partner directly with business users to understand problems, workflows, and opportunities for AI enablement.
• Translate business requirements into technical AI solution designs.
• Rapidly prototype, iterate, and productionize AI tools based on user feedback.
• API & Integration Architecture
• Design and consume REST based APIs to connect AI tools with data, services, and downstream systems.
• Build middleware or service layers that enable secure, scalable AI access to enterprise data.
• Ensure AI tools are modular, reusable, and extensible.
• AI Governance, Security & Responsible Use
• Ensure AI solutions comply with enterprise security, privacy, and data governance standards.
• Implement controls around data access, prompt safety, and output validation.
• Partner with architecture and risk teams to operationalize AI responsibly.
• Stay Current with AI Technology
• Continuously evaluate and apply the latest AI tools, frameworks, and platform capabilities.
• Bring forward best practices and emerging patterns in GenAI, copilots, and AIdriven applications.
• Help shape enterprise standards for AI solution development.

Required Skills & Qualifications

• AI & GenAI Engineering (Core)
• Hands-on experience building AI applications using LLMs (OpenAI, Azure OpenAI, or equivalent).
• Experience implementing RAG architectures and AI workflows using enterprise data.
• Strong understanding of prompt engineering, orchestration, and AI behavior tuning.
• APIs & Integration
• Strong experience designing and consuming REST APIs.
• Ability to integrate AI tools with data platforms, internal services, and third party systems.
• Experience building lightweight service layers or backend components for AI applications.
• Programming & Tooling
• Proficiency in Python and/or TypeScript/JavaScript for AI application development.
• Familiarity with modern AI frameworks and SDKs (e.g., LangChain, Semantic Kernel, similar).
• Experience deploying AI solutions in cloud environments (Azure preferred).
• Data Literacy
• Strong understanding of structured and unstructured data concepts.
• Ability to work with curated datasets, semantic layers, and metadata driven access patterns.
• Comfort integrating AI solutions with analytics and reporting data.
• Business Partnership
• Proven ability to work directly with business users and translate needs into working solutions.
• Strong communication skills across technical and nontechnical audiences.
• Productoriented mindset focused on usability and adoption.

Nice to Have

• Experience with Microsoft Copilot, Power Platform, or enterprise AI assistants.
• Familiarity with Snowflake, Databricks, or enterprise data warehouses (integration focus).
• Exposure to investment, financial, or analytical domains.