Agentic AI Lead
Location - Remote
We are looking for an experienced Agentic AI Lead to design and develop AI-driven solutions for mainframe code modernization and conversion. The ideal candidate should have strong hands-on experience with Claude Code, Java/Spring Boot, AlloyDB, and SQL.
Key Responsibilities:
- Design and build Agentic AI solutions and AI agents for mainframe code analysis and modernization.
- Use Claude Code and LLMs to develop intelligent agents for automated code conversion.
- Develop scalable backend services using Java and Spring Boot.
- Work with Google AlloyDB and SQL for data storage, processing, and integration.
- Define agent workflows, orchestration, and tool integrations.
- Lead the technical design, development, testing, and deployment of AI solutions.
- Collaborate with architects and engineering teams to deliver modernization initiatives.
- Provide technical leadership and mentor team members.
Required Skills:
- Strong experience in Agentic AI / Generative AI and LLM-based applications.
- Hands-on expertise with Claude Code.
- Strong Java and Spring Boot development experience.
- Experience with AlloyDB and SQL.
- Experience with Mainframe/COBOL code modernization or migration.
- Strong understanding of AI agents, prompt engineering, LLM integration, and agent orchestration.
Must Have: Claude Code, Agentic AI, Java, Spring Boot, AlloyDB, SQL, and Mainframe/COBOL modernization experience.
Senior AI Architect
Location - Remote
About the Role:
We are looking for a Senior AI Architect who will serve as the top AI/ML technical authority for the finance IT
organization — someone who can translate a business transformation vision into a coherent AI/ML technical
strategy, design the end-to-end architecture that makes that strategy real, and guide engineering teams through
detailed design and implementation.
This is a highly senior, hands-on architecture role. You will operate at multiple altitudes: shaping multi-year
AI/ML roadmaps with business and technology leaders, producing detailed reference architectures and design
standards, and working directly with engineers to solve hard design problems. You will be the primary
architectural voice for GenAI, machine learning, automation, and agentic systems across finance technology.
What You'll Do
Strategy & Vision
• Define the AI/ML technical strategy that underpins the finance organization's broader business
transformation vision.
• Partner with business leaders and senior stakeholders to translate financial and operational goals into
actionable AI/ML and automation initiatives.
• Establish and evangelize an “AI-first” architectural philosophy across finance technology — identifying
where GenAI, ML, and automation should be embedded by design rather than bolted on.
• Act as a trusted advisor to business and technology executives on AI/ML capability, feasibility, risk, and
value.
Architecture & Roadmap
• Create high-level automation and AI/ML architecture designs and multi-year technology roadmaps aligned
to business priorities.
• Architect AI/ML foundational frameworks and platforms that can be reused across multiple finance use
cases (fraud detection, forecasting, reconciliation, reporting, analytics, Intelligent document/content/Audio
Processing and beyond).
• Design high-performing, complex semantic layers using advanced techniques such as RAG, Knowledge
Graphs, and GraphRAG, enabling natural language query against large, complex financial databases with
extensive table relationships.
• Define reference architectures for agentic AI workflows, including multi-agent orchestration, tool use,
memory, and human-in-the-loop patterns for finance process automation.
• Set architecture and design standards, frameworks, and best practices for AI/ML solution delivery across
the organization.
Detailed Design & Engineering Guidance
• Produce detailed architecture designs — data flows, integration patterns, model serving, event-driven
pipelines, security and governance controls — that engineering teams can build against.
• Guide and mentor engineers through detailed technical design decisions, code/design reviews, and solution
build-out.
• Own architectural quality, scalability, and performance for high-volume financial data processing and
analytics pipelines.
• Evaluate and select tools, platforms, and frameworks (cloud-native and third-party) to support GenAI, ML,
and automation initiatives.
Delivery & Governance
• Ensure solutions meet enterprise, regulatory, and financial-data governance requirements (data privacy,
model risk, auditability, explainability).
• Drive adoption of MLOps/LLMOps practices for model lifecycle management, monitoring, and continuous
improvement.
• Represent AI/ML architecture in design authority / architecture review forums and champion consistent
standards across teams.
Experience
• 12+ years of progressive experience in enterprise architecture, cloud architecture, and distributed
systems/platform solutions.
• Proven hands-on experience across major cloud platforms — Azure, Google Cloud Platform, and AWS.
• Deep experience with Kubernetes and container-based platform architecture.
• Strong background in event-driven architectures/frameworks and high-volume, high-throughput data
processing systems.
• Demonstrated experience architecting GenAI and Machine Learning solutions in production environments.
• Experience in financial data processing and analytics, ideally within a finance, banking, or FinTech IT
organization.
• Track record architecting AI/ML foundational frameworks/platforms used across multiple teams or use
cases.
Technical Depth
• GenAI: LLM application architecture, prompt/context engineering, RAG, Knowledge Graphs, GraphRAG,
semantic search, vector databases.
• Agentic AI: designing and architecting complex multi-step, multi-agent agentic workflows and
orchestration patterns.
• Machine Learning: forecasting, fraud detection, anomaly detection, and predictive modeling architectures.
• Automation: business process automation and data processing automation architecture, including
intelligent/AI-augmented automation.
• Analytics AI: architecture for AI-enabled analytics and NLP-based querying against large, complex
relational data models.
• Data & Integration: large-scale data pipelines, complex database schemas, semantic layer design, API and
event-driven integration patterns.
• Cloud & Platform: Azure and Google Cloud Platform AI/ML services (e.g., Azure OpenAI, Azure ML, Vertex AI, BigQuery
ML), Kubernetes-based deployment, scalable microservices.
Leadership & Soft Skills
• Ability to operate credibly at both strategic (executive/business stakeholder) and detailed (engineering)
levels.
• Strong stakeholder management and communication skills; able to influence business leaders and technical
teams alike.
• Experience setting technical standards, design principles, and best practices at an organizational level.
• Strong mentoring and technical leadership skills — comfortable guiding engineers through detailed design
and build.
• Structured, first-principles thinker who can bring architectural rigor to ambiguous, transformation-scale
problems.
Nice to Have
• Prior experience within a Finance Technology, Banking, or regulated financial services environment.
• Experience with MLOps/LLMOps tooling and model governance/risk frameworks.
• Relevant cloud or architecture certifications (Azure Solutions Architect, Google Cloud Platform Professional Cloud Architect,
AWS Solutions Architect, TOGAF, etc.)
Lead AI Engineer Lead
Location - Remote
Below is the job description for your reference..!!
Lead AI Engineer Lead
Location: Remote
Job Description:
- Broad GenAI / Agentic AI engineers who can work across multiple business areas—not just Finance or Risk—including Procurement, FP&A, Enterprise Risk Management, Legal, Treasury, and other enterprise functions.
- • Must have strong RAG expertise, including Deep/Semantic RAG, Graph RAG, Hybrid RAG, vector databases, and complex document/data retrieval patterns.
- • Looking for experience beyond basic chatbots: agentic workflows, end-to-end automation, event-driven architecture, APIs, enterprise integrations, email/Graph API integrations, notifications, web/internet search through LLMs, and voice-to-text use cases.
- • Engineers will need to plug into and build on the client’s existing AI platform, rather than create isolated solutions. Their Azure AI platform runs on AKS with shared services such as SSO and centralized/externalized prompt management.
- • MongoDB/vector search is being used across many use cases. Azure is the more mature environment, but they are also actively building on Google Cloud Platform with GKE, Vertex AI, BigQuery, and PostgreSQL.
- • Overall, she wants people who can blend into the existing AI engineering team, understand reusable platform patterns, and deliver production-grade enterprise AI solutions across multiple use cases.
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