AI Evangelist/Senior Agentic AI Solutions Architect

Job description:-

AI Evangelist

New York City, NY Local preferred 4 days onsite per week
Full time Opportunity
AI Evangelist -Hands-On:
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 preferred 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, * 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 (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

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

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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 Langchain, Langraph, LlamaIndex, Graph RAG
• 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.

Job Title: Senior Agentic AI Solutions Architect
Location: New York City, NY Local preferred -Hybrid Role
FTE role
Job Description: We re seeking a hands-on AI architect/engineer and leader to design, build, and deliver agentic AI systems for complex financial workflows. This role combines deep technical execution with strategic communication you ll code, prototype, and lead conversations with senior stakeholders.
What You ll Do:

• Architect and implement multi-agent orchestration for investment, risk, and compliance workflows.
• Build LLM-powered pipelines with advanced RAG, tool routing, and orchestration graphs.
• Develop Python-based microservices using LangChain, Hugging Face, PyTorch, FastAPI.
• Integrate with financial data sources (Bloomberg, Refinitiv) and risk engines.
• Implement trust, safety, and compliance guardrails for regulated environments.
• Lead client workshops, executive briefings, and solution roadmaps translate technical complexity into business clarity.

Must-Have:

• Expert in Python frameworks, LLMOps/MLOps, and cloud-native deployment.
• Strong knowledge of financial instruments, risk modeling, and regulatory controls.
• Proven ability to ship complex AI systems end-to-end and communicate with leadership.

Additional Job Details:

About Tanisha Systems, Inc.

Tanisha Systems, founded in 2002 in Massachusetts-*, is a leading provider of Custom Application Development and end-to-end IT Services to clients globally. We use a client-centric engagement model that combines local on-site and off-site resources with the cost, global expertise and quality advantages of off-shore operations. We deliver Custom Application Development, Application Modernization, Business Process Outsourcing and Professional IT Services from office locations in * and *.
Tanisha Systems services clients in Government, Banking & Financial Markets, Insurance, Healthcare, Retail & Consumer Goods, Energy & Utilities, Life Sciences, Telecom, Manufacturing and Transportation Industries around the globe. Our engagement model provides a flexible operational environment that empowers our clients with the right levels of control.

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