Python, LLM Intergration/Senior Consultant Specialist

Worldwide | Oct. 15, 2026

Report as Closed

Company: HSBC Group

Country: Worldwide

Type: Remote

Employment: Full Time

Description:

Some careers shine brighter than others.

If you’re looking for a career that will help you stand out, join HSBC and fulfil your potential. Whether you want a career that could take you to the top, or simply take you in an exciting new direction, HSBC offers opportunities, support and rewards that will take you further.

HSBC is one of the largest banking and financial services organisations in the world, with operations in 64 countries and territories. We aim to be where the growth is, enabling businesses to thrive and economies to prosper, and, ultimately, helping people to fulfil their hopes and realise their ambitions.

We are currently seeking an experienced professional to join our team in the role of Senior Consultant Specialist

Role purpose

Lead the end-to-end Engineering and delivery of enterprise-grade platforms, with a strong focus on production AI (LLMs, RAG, agent workflows) and audit-ready, human-in-the-loop decisioning. Accountable for technical design, engineering standards, delivery quality, and stakeholder outcomes—while remaining hands-on in code

In this role, you will (Key responsibilities)

• Delivery leadership
  • Lead technical design reviews, sprint planning, release planning, and delivery execution.
  • Remain accountable for post-release quality, stability, and continuous improvement.
  • Drive engineering standards for code reviews, documentation, and maintainable patterns.
• AI/LLM platform engineering
  • Build production AI capabilities using LLMs, RAG, semantic search, and vector embeddings.
  • Implement explainability and controls to reduce hallucination risk and support compliance (e.g., cited outputs, traceable reasoning, gated approvals).
  • Establish human-in-the-loop checkpoints for exception handling and operational assurance.
• Backend & data engineering
  • Build and maintain robust API backends (e.g., Python/FastAPI), data stores (e.g., PostgreSQL), and retrieval layers (vector search).
  • Implement pipelines that ingest unstructured content (documents, notes, recordings), structure findings, and expose analytics/KPIs.
• Stakeholder & cross-functional collaboration
  • Partner with Directors, Product Owners, and business stakeholders to shape priorities and roadmaps.
  • Translate business controls (e.g., finance review, hiring rubrics, audit rules, clinical protocols) into implementable technical solutions.
• People leadership
  • Manage, mentor, and develop engineers across application development, backend services, and production AI.
  • Participate in hiring, interviewing, onboarding, and coaching engineers into advanced technical roles.
• Typical outcomes / impact measures (examples aligned to profile)
  • Reduce manual review effort (e.g., hours/day down to minutes) through automation with verification gates.
  • Improve decision consistency and auditability via cited, explainable AI outputs.
  • Increase throughput and reduce rework by enforcing rule-based validations and escalation workflows.
  • Deliver dashboards and operational KPIs for branch/operations performance and recurring issue analysis.

Skills & Experience

• Required experience
  • ~10+ years building and shipping enterprise software; strong record of leading delivery and architecture.
  • Proven experience designing production AI systems (LLMs, RAG, semantic retrieval) with governance and audit trails.
  • Experience leading and mentoring multi-disciplinary engineering teams (10+; ideally up to ~20+).
  • Strong experience building backend platforms and APIs in Python (e.g., FastAPI) and designing data models in PostgreSQL.
  • Experience operating in regulated or high-assurance environments (finance, hiring, healthcare, audit workflows).
• Required technical skills
  • Languages/Frameworks: Python, FastAPI, REST APIs
  • Data: PostgreSQL, vector embeddings, semantic search
  • AI: LLM integration, RAG, agent orchestration (e.g., LangGraph-style patterns), explainability and evaluation practices
  • Cloud: AWS (including ma

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