AI Engineer / Lead / Architect | 100% Remote Role | 3Roles

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.