Galent
This range is provided by Galent. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.
Base pay range $130,000.00/yr - $140,000.00/yr
Recruitment Lead (US & Canada IT Recruitment) Location:
New York (3 Bryant Park New York, NY – 10036) – 4 Days onsite Role
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 senior role involves technical development as well as 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.
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 (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.
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, explainability, and compliance at scale.
Expertise in code review, automated testing, and documentation standards for mixed human/AI development environments.
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 models.
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.
Graph database -RD4j, Neo4j and timeseries database.
Embeddings & Vector Databases: Understanding embeddings, vector search, vector DB platforms (FAISS, Pinecone, Chroma, Weaviate), 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
Sourcegraph 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/GitLabs (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, RunwayML) 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.
Seniority level Mid‑Senior level
Employment type Full‑time
Job function Information Technology
Industries Technology, Information and Media and Financial Services
#J-18808-Ljbffr
Base pay range $130,000.00/yr - $140,000.00/yr
Recruitment Lead (US & Canada IT Recruitment) Location:
New York (3 Bryant Park New York, NY – 10036) – 4 Days onsite Role
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 senior role involves technical development as well as 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.
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 (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.
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, explainability, and compliance at scale.
Expertise in code review, automated testing, and documentation standards for mixed human/AI development environments.
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 models.
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.
Graph database -RD4j, Neo4j and timeseries database.
Embeddings & Vector Databases: Understanding embeddings, vector search, vector DB platforms (FAISS, Pinecone, Chroma, Weaviate), 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
Sourcegraph 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/GitLabs (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, RunwayML) 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.
Seniority level Mid‑Senior level
Employment type Full‑time
Job function Information Technology
Industries Technology, Information and Media and Financial Services
#J-18808-Ljbffr