Job Title: AI Evangelist
Location: New York, NY (4 Days onsite role)
Duration: Full Time
Job Description:
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 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 another JS framework for UI development
• Strong hands-on experience with Python, SQL, and AI/ML frameworks (e.g., 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.

Desired Traits:

• Passion for driving innovation and adoption of AI in highly regulated settings.
• Effective at stakeholder education—from boardroom to engineering teams.
• Strong problem-solving mindset, able to translate technical outputs into practical business recommendations.
• Integrity and diligence, with a commitment to both organizational objectives and ethical AI deployment.

Essential Experience & Skills:

• Advanced Python expertise, plus experience with other major backend languages (e.g., 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, explain ability, and compliance at scale
• Expertise in code review, automated testing, and documentation standards for mixed human/AI development environments

Desired Traits:

• Analytical mindset with a passion for innovation, experimentation, and best practices in AI-enhanced software engineering
• Effective communicator, comfortable translating complex AI behaviors and code-gen strategies for both technical and business audiences
• Champion of ethical AI development, security consciousness, and responsible agent operation in critical production settings.

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 for high recall and precision
• Agentic Design: Orchestrating LLM-driven agents capable of multi-step reasoning, tool use, and autonomous state management—including fallback strategies for error
• Production Deployment: Packaging models and agentic systems as scalable APIs, with robust pipelines for latency, concurrency, and failure isolation, including container orchestration or serverless deployment
• LLM Optimization: Applying quantization, pruning, and distillation to optimize performance and cost; benchmarking for speed, accuracy, and hardware utilization
• Observability & Monitoring: Implementing logging, tracing, dashboards, and alignment monitoring for prompts, responses, and agent behaviors
• Core SDLC AI Integration: Using generative AI for requirement refinement, technical design blueprinting, architecture review, API and schema auto-generation, and cross-functional artifact production
• Security & Compliance: Building guardrails to enforce data privacy, compliance with regulations, and responsible use of LLMs, particularly in sensitive or regulated environments.
• Modern Deep Learning: Mastery of frameworks including TensorFlow, PyTorch, and HuggingFace Transformers, with proven expertise in transformers, CNNs, RNNs, and attention mechanisms for custom and state-of-the-art model

Essential AI Tools:

• GitHub Copilot: Mainstream AI-powered code generation and completion for major languages, widely integrated into enterprise SDLC
• ChatGPT/GPT-4/Vision: Prompt-driven code assistance, architecture brainstorming, documentation generation, and natural language requirement mapping
• SonarQube: AI-powered static code analysis and vulnerability detection for code security and quality assurance across SDLC.synapt+1
• Jira (with AI plugins): AI-enhanced project management, backlog refinement, and sprint planning—crucial for orchestrating product delivery at scale
• Claude Code: Multi-step code generation and agentic orchestration, especially suitable for agent-based SDLC
• Datadog and Dynatrace: Proactive AI in monitoring, predictive analytics, and incident response for production reliability and observability.
• RAG frameworks like Lang chain, Lan graph, Llama Index, Graph RAG
• Graph database -RD4j, Neo4j and timeseries database
• Embeddings & Vector Databases: Understanding embeddings, vector search, vector DB platforms (FAISS, Pinecone, Chroma, We aviate), and semantic retrieval
• Observability & Evaluation: Setting up logging, debugging, and automated quality evaluation for RAG applications (e.g., with TruLens, Streamlit dashboards).
• Containerization/DevOps: Packaging with Docker or similar, using cloud/AWS/Azure integrations for scalable deployments.

Nice to Have AI Tools:

• Source graph Cody: LLM-powered search, code context awareness, and auto-completion over vast enterprise repositories.
• Cursor/Codex/Windsurf: AI-native development workflow management; designed for large-scale coding and agentic workflows
• Amazon Q (AWS): AI-driven code, architecture recommendation, and AWS-native code and cloud resource management
• Synapt SDLC Squad: Multi-agent generative AI platform for end-to-end SDLC automation, code review, and compliance.
• Bitbucket/GitLab's (with AI modules): AI-assistance in code review, merge requests, release management, and security
• Figma (AI for Design/Prototyping): AI documentation, prototyping, and developer handoff for frontend/UI

Nice to Have architecture Skills:

• Lightweight Architecture ADLs: Using architecture definition languages (ADLs) to leverage LLMs for generating structural constraints, fitness functions, and architecture governance
• Automated UI/UX Prototyping: Leveraging generative tools (e.g., Claude, Runway ML) for rapid wireframe and design generation from requirements or stakeholder
• End-to-End SDLC Automation: Experience with multi-agent platforms and orchestration tools for automating requirement gathering, code review, compliance, and release management.linkedin+1
• Cost-Efficiency & Sustainability: Implementing sustainable AI practices, carbon-aware scheduling, and model lifecycle management for production
• Emergent LLM Platforms: Experience integrating and orchestrating new LLMs, open-source agents, vector DBs, and hybrid architectures beyond mainstream offerings
• Ethical AI & Governance: Leading the definition of internal standards and policies for responsible, bias-safe LLM operation and agentic workflows
• Domain-Specific Knowledge: Deep knowledge of applying LLMs and generative AI in specialized contexts, such as finance, Banking, or other regulated domains.