algoleap
Website:
algoleap.com
Company:
https://www.linkedin.com/company/algoleap
Seniority: Not Applicable
Industries: IT Services and IT Consulting
Job details:
Role Overview
This is a strategic leadership and hands-on architecture role responsible for defining, building, and scaling enterprise-wide Automation, GenAI, and Agentic AI capabilities.
The role combines enterprise automation leadership, solution architecture, business consulting, innovation, and delivery ownership. You will lead the organization's transition from traditional automation (RPA and workflow automation) to intelligent, AI-driven, and agentic automation platforms.
As the senior-most technical leader in the function, you will engage directly with business leaders, define AI and automation strategy, architect enterprise solutions, deliver rapid proof-of-concepts, and guide production implementation. This is not a supervisory management role—it requires deep technical expertise, hands-on development, strong business acumen, and the ability to influence enterprise-wide transformation.
You will operate across multiple concurrent initiatives, balancing strategic leadership with active solution design and development.
Key Responsibilities 2. Business Engagement & Advisory 3. Solution Architecture & Technical LeadershipDesign Responsibilities
- Enterprise Automation & AI Leadership
- Define and execute the enterprise Automation, GenAI, and Agentic AI strategy and roadmap.
- Drive the evolution from traditional RPA to Intelligent Automation and Agentic AI.
- Establish enterprise-wide governance, architecture standards, security controls, and best practices.
- Build automation and AI capabilities as strategic business differentiators.
- Continuously improve the value proposition of the Automation CoE.
- Identify opportunities for innovation and measurable business value realization.
- Develop enterprise adoption strategies across business functions and geographies.
- Partner with Business Leaders, Transformation Leaders, and Technology Executives to understand challenges and identify automation and AI opportunities.
- Conduct feasibility assessments and recommend the most appropriate approach:
- Pro-Code Solutions (LangGraph, AgentCore, custom AI applications)
- Low-Code Solutions (Copilot Studio, Power Automate, RPA)
- Document and communicate solution recommendations with clear business and technical rationale.
- Present AI and automation opportunities, roadmaps, business cases, and value realization metrics to senior leadership.
- Challenge assumptions through data-driven discussions and innovative thinking.
- Define end-to-end architecture for Agentic AI, GenAI, Intelligent Automation, and Enterprise Automation platforms.
- Lead architecture design on:
- AWS AgentCore
- AWS Bedrock
- LangGraph
- LangChain
- MCP-based ecosystems
- Agent-to-Agent (A2A) architectures
- Multi-agent orchestration
- Agent state management
- Conditional routing
- Human-in-the-loop (HITL) workflows
- Memory architecture
- Tool orchestration
- Knowledge management
- Security and compliance architecture
- API and integration architecture
- Enterprise-scale observability and monitoring
Ensure all solutions align with enterprise security, governance, compliance, and operational standards.
- AI & Agentic Solution Engineering
Lead Architecture And Implementation Of
Agentic AI Platforms
- Multi-agent systems
- Autonomous workflows
- Agent collaboration patterns
- Agent memory architectures
- Agent governance frameworks
Retrieval-Augmented Generation (RAG)
Design And Implement
- Traditional RAG
- Agentic RAG
- Hybrid Search
- Re-ranking architectures
- Knowledge Graph RAG
- Graph-enhanced retrieval systems
Select and justify architecture patterns based on business requirements.
- Systems Integration & Enterprise Connectivity
Design Integration Strategies For
- SAP
- Salesforce
- Enterprise applications
- Data platforms
- Internal business systems
- Third-party platforms
Expertise Required In
- REST APIs
- Event-driven architectures
- MCP Server/Client patterns
- A2A communication frameworks
- Enterprise integration patterns
- Workflow orchestration
6. Delivery Leadership & Value Realization
- Own delivery outcomes across multiple concurrent automation and AI initiatives.
- Deliver working Proof of Concepts (POCs) within 1–4 weeks.
- Evaluate POCs developed by internal teams or vendors and determine scale, redesign, or rebuild strategies.
- Define implementation roadmaps and production readiness criteria.
- Track:
- ROI
- Adoption
- Productivity gains
- Cost savings
- Business impact
- Drive continuous optimization and improvement.
7. Hands-On Development & Engineering Excellence
This is a coding and architecture role.
You Will
- Build critical and complex solution components directly.
- Develop production-grade Python applications.
- Lead implementation of advanced AI orchestration frameworks.
- Review, optimize, and troubleshoot production systems.
- Leverage AI-assisted development tools while maintaining full ownership of generated code.
AI Development Tools
Experience With
- Claude Code
- GitHub Copilot
- Cursor
- Cline
- Similar AI engineering tools
8. Team Leadership & Capability Building
Lead and mentor Solution Leads, Developers, Associates, and Automation Engineers.
Responsibilities Include
- Establishing architecture standards
- Coaching teams on implementation patterns
- Developing future-ready AI and automation skills
- Creating a high-performance engineering culture
- Enabling delivery teams through clear architecture and decision frameworks
9. Vendor, Platform & Commercial Management
- Manage automation and AI vendor ecosystems.
- Evaluate platform investments and licensing strategies.
- Optimize technology costs and consumption.
- Drive innovation through strategic partnerships.
- Assess emerging technologies and determine adoption suitability.
10. Innovation & Future-State Technology Leadership
Continuously Evaluate
- Agentic AI frameworks
- Foundation model ecosystems
- AI infrastructure platforms
- Emerging enterprise automation technologies
Provide Evidence-based Recommendations On
- Adoption timing
- Enterprise fit
- Risk assessment
- Scalability
- Long-term technology strategy
Champion innovation internally and externally through demonstrations, thought leadership, and transformation initiatives.
Required Skills & Experience
Agentic AI & GenAI
Must Have
- AWS AgentCore (Runtime, Memory, Tools Gateway)
- LangGraph (Multi-agent orchestration, checkpointing, conditional routing, HITL)
- LangChain
- AWS Bedrock
- Agentic AI architecture and implementation
- Multi-agent systems
- AI governance and guardrails
Enterprise Automation
Strong Experience With
- Automation Anywhere
- UiPath
- Power Automate
- Microsoft Copilot Studio
- Workflow automation platforms
- Intelligent Document Processing (IDP)
AI Data & Knowledge Systems
- SQL and advanced database design
- NL-to-SQL architectures
- Semantic search
- Vector databases
- Snowflake (preferred)
- Graph databases (Neo4j preferred)
- Knowledge graph architectures
Integration & Enterprise Architecture
- REST APIs
- Event-driven architecture
- MCP Server/Client
- A2A protocols
- SAP integration
- Salesforce integration
- Enterprise application integration
AI Platform & Cloud Expertise
Primary
Preferred
- Google Agentspace
- Google Cloud AI
- Azure AI Foundry
- Azure OpenAI
Software Engineering
- Expert-level Python
- Docker
- CI/CD
- Production deployment
- Monitoring and observability
- Performance optimization
- Cost governance
Model & AI Platform Strategy
Experience Evaluating And Deploying
- OpenAI GPT models
- Anthropic Claude
- Google Gemini
- Meta Llama
Ability To Define
- Model selection strategies
- Cost optimization approaches
- Governance frameworks
- Provider risk management
Business & Leadership Skills
- Executive stakeholder management
- Business case development
- Strategic planning
- Transformation leadership
- Value realization management
- Vendor management
- Change management
- Executive communication
Preferred Qualifications
- AWS Solutions Architect Professional Certification
- Google Professional Cloud Architect Certification
- Azure Solutions Architect Expert Certification
- Experience in Legal, Financial, Risk, Compliance, or Regulatory domains
- Experience with Azure Document Intelligence or AWS Textract
- Fine-tuning experience (LoRA, QLoRA)
- Production MCP implementations
- Production Agent-to-Agent (A2A) implementations
Experience
- 10+ years overall technology experience
- 5+ years leading enterprise automation programs
- 3–5 years solution architecture ownership with delivery accountability
- Proven experience delivering production-grade Agentic AI solutions
- Demonstrated success scaling enterprise automation capabilities across multiple business functions
What Success Looks Like
- Enterprise-wide adoption of automation and Agentic AI
- Rapid delivery of high-value AI solutions
- Strong governance and scalable architecture standards
- Measurable business value and ROI realization
- High-performing, future-ready engineering teams
- Automation and AI established as strategic differentiators for the organization
Click on Apply to know more.
