AIML Engineer

Hi All

Please find below JD and revert back with updated CV to [Confidential Information]

AI Engineer:

Candidates need a strong foundational understanding of agentic solutions, including how to design and implement an end-to-end agentic architecture. They must demonstrate during the interview how they would approach an agentic AI solution and provide a lot of detail and depth as they do. Depth must include not only a foundational understanding of how to implement the solution, but also what the purpose is, tools used and how. They also must show how they understand the proper components, context, guardrails, what python frameworks to use, how the solution can be hosted, what tools are used and what those tools do.

Typical AI interviews start at a surface level, but prior candidates could not clearly describe the components or purpose of an agentic solution, which was a major disqualifier. AI candidates also need enough polish and communication ability to work with stakeholders.

Project Intel

• Forward-Deployed AI Engineering

o    Partner directly with bankers to identify and prioritize high-impact AI use cases across deal workflows

• Agentic Workflow Development

o    Build and deploy multi-step AI agents to improve productivity across sourcing, execution, and analysis

• AI-Generated Deal & Marketing Materials

o    Automate creation of pitch decks, CIMs, and other client-facing content using AI

• Claude Skills, Plugins & Data Integration

o    Develop domain-specific AI skills and connect them to internal data sources using MCP and plugins

• Financial Modeling Augmentation

o    Enhance and streamline banker modeling workflows with AI-assisted analysis

• Rapid Prototyping & Productionization

o    Deliver solutions in short iterative cycles and scale successful use cases into production

• AI-Driven Prospecting

o    Build workflows to identify and prioritize target companies and opportunities

• Product-Led Execution

o    Execute against a structured backlog, translating requirements into technical solutions under product leadership

Experience

• 3–5+ years in data/engineering roles, with 3+ years focused on AI engineering
• Proven track record of deploying and scaling agentic systems in production
• Experience working in regulated environments with governance and security constraints

Required Skills (Priority Order)

• Agentic Application Development – Hands-on experience building multi-step agents, tool-use patterns, and prompt chaining using frameworks such as Anthropic SDK, LangChain, Microsoft Agent Framework, and Pydantic AI
• Claude Ecosystem Expertise – Experience developing Claude Skills, plugins, connectors, and Cowork agents in production environments
• Cross-Platform Agent Development – Ability to migrate and adapt agent workflows across platforms (e.g., Claude ↔ Copilot Studio)
• MCP Integration – Experience building MCP servers, integrating tools/data sources, and managing authentication and response handling
• Python – Strong proficiency for AI/ML application development and system integration

Key Responsibilities

• Design and deploy multi-step agentic workflows in production environments (not just POCs)
• Build AI-powered solutions supporting investment banking workflows (deal sourcing, company matching, pitch materials, financial modeling augmentation)
• Develop and integrate Claude-based agents, skills, connectors, and plugins
• Implement secure, governed AI systems with appropriate guardrails for regulated environments
• Build and integrate MCP servers to connect LLMs with internal tools and data sources
• Translate product requirements into technical implementations in partnership with product leadership
• Act as a forward-deployed engineer, collaborating directly with bankers to identify and prioritize AI use cases
• Rapidly prototype and productionize solutions in iterative cycles (weeks to days)