Junior AI Engineer

Responsibilities

• Build agentic & GenAI tools for Financial Markets use cases
• Develop agentic AI workflows that can plan, execute, and validate steps across operational processes (e.g., exception triage, document checks, workflow routing, and knowledge-assisted decisioning)
• Build GenAI features such as summarization, classification, extraction, drafting, and Q&A for financial operations, with a focus on reliability and traceability
• Create RAG pipelines to ground LLM outputs in approved knowledge sources (policies, SOPs, regulatory artifacts, client documentation, historical cases)
• Engineer production-grade integrations
• Implement tool-calling / function-calling patterns so agents can safely interact with internal systems (case management, CRMs/ERPs, data services, document repositories) and produce structured outputs
• Build backend services and APIs that package AI capabilities into reusable components (internal tools and/or client-facing solutions)
• Write clean, maintainable code with strong engineering hygiene: unit tests, integration tests, code review participation, CI/CD readiness, and documentation
• Design lightweight evaluation harnesses and test suites (golden datasets, regression tests, prompt/version tracking, and error analysis) to continuously improve accuracy and consistency
• Implement guardrails and human-in-the-loop checkpoints where appropriate (especially for compliance-sensitive decisions)
• Collaborate with domain SMEs (ops, compliance, risk) to translate requirements into measurable system behavior and acceptance criteria

Requirements

• 2+ years’ experience building production software with strong hands-on coding ownership
• Strong Python programming skills with comfort working in a shared professional codebase; familiarity with Java or TypeScript/JavaScript is a plus
• Foundational understanding of LLMs: attention mechanisms, tokenization, context windows, and inference trade-offs
• Practical experience with LLM/GenAI development: prompt and context design, structured outputs, tool calling, and agentic workflows
• Familiarity with common engineering practices: Git, REST APIs, logging basics, and data handling (JSON, SQL)
• Ability to work in ambiguous problem spaces and iterate quickly with feedback from product and ops stakeholders

Qualifications

• Personal projects, open-source contributions, or research on ML or applied ML
• Experience building RAG systems: chunking strategies, embedding models, vector stores (e.g. FAISS), and retrieval evaluation
• Hands-on experience with agentic orchestration frameworks such as LangGraph or LangChain-style state machines, deployed into real APIs or services
• Exposure to financial services operations (capital markets ops, KYC/ client lifecycle, AML/compliance, reconciliations, exceptions processing)
• Experience with intelligent document processing pipelines: PDF parsing, schema mapping, and field validation
• Familiarity with deep learning frameworks (PyTorch, TensorFlow, or JAX) and strong applied ML fundamentals
• Knowledge of responsible AI concepts: privacy, explainability, and evaluation discipline
• Full-stack development experience (e.g. React, Next.js, FastAPI) is a meaningful bonus