Tanisha Systems, Inc · 3 weeks ago
AI Evangelist/Senior Agentic AI Solutions Architect
new york, NY
Full-time
Hybrid
Mid, Senior Level
4+ years expTanisha Systems, Inc. is a leading provider of Custom Application Development and IT Services globally. They are seeking a hands-on AI architect/engineer to design, build, and deliver agentic AI systems for complex financial workflows, combining technical execution with strategic communication.
Information TechnologyMobileSoftware
Responsibilities
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
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
Qualification
Required
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
Demonstrated ability to manage multiple priorities and projects while maintaining strategic alignment and rigorous attention to detail
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
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
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
Company
Tanisha Systems, Inc
Tanisha Systems, founded in 2002 in Massachusetts-USA, is a leading provider of Custom Application Development and end-to-end IT Services to clients globally.
1001-5000 employees
H1B Sponsorship
Tanisha Systems, Inc has a track record of offering H1B sponsorships. Please note that this does not
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Trends of Total Sponsorships
2025 (71)
2024 (55)
2023 (57)
2022 (96)
2021 (125)
2020 (123)
Funding
Current Stage
Late StageLeadership Team
Subba Badeti
CEO - Direct CLients
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