Job Specifications
A hands-on AI Evangelist in a financial organization plays a vital role bridging the gap between cutting-edge AI technologies and practical business needs. This is a senior role that not only involves technical development but also demands effective communication and advocacy to facilitate the responsible adoption of AI in finance.
Key Responsibilities as Evangelist
Build and demonstrate AI-powered solutions for financial applications preferably in investment banking, Trading or insurance environments.
Translate complex AI concepts into actionable business value for both technical and non-technical stakeholders, simplifying information for internal teams and executive leadership.
Lead workshops, seminars, and training sessions for teams across the organization, promoting AI literacy and upskilling staff in banking, investment, or insurance environments.
Ability and/or experience in authoring technical blogs, white papers, and internal documentation that explain the impact and possibilities of AI in the financial domain.
Experience on working on advisory capacity to CxO, Head of Engineering, Head of Architecture on technical strategies
Act as a visible presence at industry conferences, webinars, and external forums to position the organization as a leader in responsible AI use within finance.
Partner with compliance, risk, and IT teams to ensure all AI solutions meet strict regulatory and ethical standards prevalent in financial services.
Prototype, test, and deploy AI models that address market forecasting, customer insights, automated underwriting, or anti-money laundering strategies.
Key Technical and Design Responsibilities
Build, deploy, and manage both agentic AI architectures and generative code systems, ensuring scalable and secure integration of technologies like LLMs, code generators, and automation agents within production workflows.
Oversee technical design, implementation, and code reviews-especially for code created or assisted by AI tools-maintaining high standards of security, performance, and maintainability in Python and other programming languages.
Develop robust testing and validation protocols for AI-generated code and agent behavior, including prompt engineering, debugging, and post-deployment monitoring for unusual failure patterns or compliance issues.
Lead technical teams, mentor junior engineers, and set excellence in software engineering practices; create documentation and establish guidelines for both human and AI-driven contributions
Collaborate with cross-functional stakeholders (DevOps, security, product, and business leaders) to ensure rapid, safe adoption of agentic and generative AI features
Essential Qualifications
Bachelor's or master's degree in computer science, Data Science, Finance, or related field.
Experience in one or many of the high-level programming languages like C++, Java, C#
Good understanding of Typescript, Node.js and other JS framework for UI development
Strong hands-on experience with Python, SQL, and AI/ML frameworks (eg, TensorFlow, PyTorch) as applied to financial data and workflows
At least 4+ years working in AI roles within finance, fintech, or technical consulting, preferably with exposure to regulatory environments.
Deep knowledge of AI ethics, compliance, Guardrails, data privacy, and compliance trends relevant to the financial sector.
Excellent communication, stakeholder engagement, and technical storytelling abilities.[7][3]
Demonstrated ability to manage multiple priorities and projects while maintaining strategic alignment and rigorous attention to detail.
Essential Experience & Skills
Advanced Python expertise, plus experience with other major Back End languages (eg, Java, C++, Go) and modern AI/ML toolkits
Demonstrated proficiency in designing, validating, and launching code-generation systems and agentic workflows, strong familiarity with prompt engineering and AI model deployment
Track record of hands-on technical leadership within agile teams, overseeing both human and AI-generated codebases and ensuring auditability, explainability, and compliance at scale
Expertise in code review, automated testing, and documentation standards for mixed human/AI development environments
Essential AI Design and Architecture Skills
Prompt Engineering: Crafting structured prompts to drive deterministic and reproducible outputs from LLMs, using techniques like chain-of-thought and few-shot prompting
Context Engineering: Dynamically injecting relevant external data into prompts; designing and managing context windows, handling retrieval noise and context collapse in long-context
Fine-Tuning & Model Adaptation: Using methods like LoRA/QLoRA for domain adaptation, managing data curation pipelines, and monitoring overfitting versus generalization-especially in high-stakes environments
Retrieval-Augmented Generation (RAG): Building LLM workflows with external knowledge integration, engineering embeddings and retrieval pipelines