AI Technical Manager
Biz4Group LLC
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Job summary
Biz4Group LLC, an AI-first technology consulting and product engineering company, is seeking an AI Technical Manager to steer AI-powered product development and enterprise applications. You’ll lead engineering teams, set architecture direction and drive responsible AI adoption to deliver business outcomes.
You’ll work across backend, AI, APIs and cloud infrastructure, emphasize MCP-based integrations, MCP servers, and AI-assisted development while partnering with Product, Sales and Delivery to
Qualifications
- 9+ years of software engineering experience.
- 5+ years of technical/team leadership experience.
- Experience building and deploying AI-powered applications.
- Strong understanding of system architecture and enterprise integrations.
- Experience with MCP servers or similar AI tool-integration architectures.
- Experience working with cloud platforms and modern DevOps practices.
- Experience working with product, delivery and customer-facing teams.
- Experience in SaaS, PropTech, HealthTech, FinTech, enterprise software or other domain-driven products is an advantage.
Responsibilities
- Lead engineering teams building AI-powered products and enterprise solutions.
- Define scalable and maintainable application architectures and lead architecture decisions across backend, AI, APIs, databases and cloud infrastructure.
- Lead API-first, event-driven and integration-heavy architectures and review critical design decisions.
- Ensure security, scalability, maintainability and performance from the start.
- Develop MCP servers and connect AI agents with enterprise applications and internal tools.
- Promote high-quality AI-assisted development and Vibe Coding and measure adoption impact.
- Collaborate with Product, Sales, Delivery and customers to translate business problems into technical solutions.
Skills
Tools
Job description
Biz4Group is an AI-first technology consulting and product engineering company building enterprise-grade software and AI solutions for startups, SMBs and Fortune 500 companies.
Our work spans AI agents, enterprise automation, SaaS platforms, PropTech, HealthTech, FinTech and custom software development.
We are looking for an AI Technical Manager who can lead engineering teams through the next phase of software development, where AI is becoming part of how products are designed, built, tested and delivered.
This role is for someone who combines strong engineering fundamentals, AI expertise, product thinking and business understanding.
As an AI Technical Manager, you will be responsible for the technical direction and engineering execution of AI-powered products and enterprise applications.
You will lead teams, make architecture decisions, drive responsible AI adoption, improve engineering productivity and ensure that technology decisions are aligned with customer needs and business outcomes.
This is not a traditional people-management role.
We are looking for someone who can think like a product owner, operate like an architect, lead like an engineering manager and execute with the speed required in a fast-moving AI company.
- Lead engineering teams building AI-powered products and enterprise solutions.
- Drive practical adoption of AI across the software development lifecycle.
- Establish standards for AI-assisted development, testing, code review and delivery.
- Identify where AI can improve engineering productivity, quality and speed.
- Mentor engineers on modern AI-native development practices.
- Build a culture of ownership, technical excellence and continuous learning.
- Define scalable and maintainable application architectures.
- Lead architecture decisions across backend, AI, APIs, databases and cloud infrastructure.
- Lead API-first, event-driven and integration-heavy architectures.
- Review system design, technical decisions and critical code.
- Ensure security, scalability, maintainability and performance are considered from the beginning.
Lead the development and integration of production-grade AI systems, including:
- LLM-powered applications
- AI agents
- Agentic workflows
- Multi-agent systems
- Retrieval-Augmented Generation (RAG)
- AI orchestration
- Model and tool integrations
- AI evaluation and monitoring
Work with models and platforms across OpenAI, Anthropic, Google and open-source ecosystems where appropriate.
The focus should always be on solving the business problem first and selecting the right AI approach second.
Take ownership of modern AI-to-system integration using Model Context Protocol (MCP).
Responsibilities include:
- Design and develop MCP servers.
- Connect AI agents with enterprise applications and internal tools.
- Enable AI systems to work with CRMs, ERPs, databases and business applications.
- Design tool-use and orchestration architectures.
- Implement authentication, authorization and permission controls.
- Establish standards for secure and maintainable MCP implementations.
- Identify opportunities where MCP can simplify enterprise AI integrations.
AI-assisted development is becoming a core engineering capability at Biz4Group.
You will:
- Promote high-quality AI-assisted development and Vibe Coding.
- Help teams use AI coding tools effectively without compromising engineering standards.
- Define practices for prompting, code generation, review and validation.
- Ensure AI-generated code meets production standards.
- Introduce automated testing and validation wherever possible.
- Measure whether AI adoption is actually improving development speed and quality.
The goal is not simply to write more code faster.
The goal is to build better software faster without sacrificing quality.
Technology decisions should always connect back to the customer's business problem.
You will:
- Understand the problem before recommending technology.
- Convert business requirements into scalable technical solutions.
- Work closely with Product, Sales, Delivery and customers.
- Identify technical approaches that improve customer value.
- Balance speed, quality, cost and long-term maintainability.
- Identify opportunities to turn repeated project requirements into reusable products, components or platforms.
- Help teams move from a project-delivery mindset to a product-delivery mindset.
- Lead, mentor and develop engineering teams.
- Build technical capability across AI, architecture and modern development.
- Participate in technical hiring and interviews.
- Identify skill gaps and create development plans.
- Establish clear technical ownership within teams.
- Support engineers in solving complex technical problems.
- Build a culture where engineers take ownership instead of waiting for instructions.
- Ensure projects are technically ready for development before execution begins.
- Identify technical risks and dependencies early.
- Improve engineering estimation and delivery predictability.
- Establish strong testing and QA practices.
- Reduce production defects and technical rework.
- Ensure proper documentation and knowledge transfer.
- Maintain high standards for customer experience and production reliability.
- LLM integration
- AI agents
- Agentic AI
- Multi-agent systems
- RAG
- Embeddings
- AI evaluation
- Agent orchestration
- AI observability and monitoring
Strong practical understanding of:
- Tool integration
- Tool orchestration
- Context management
- AI-to-enterprise system integrations
- Authentication and authorization for AI tools
Experience with several of:
- FastAPI
- Django
- Flask
- Node.js
- Express.js
- REST APIs
- GraphQL
- WebSockets
Experience with:
- AWS, Azure or Google Cloud
- Docker
- CI/CD
- Infrastructure as Code
- GitHub Actions
- Monitoring and observability
Experience with:
- MongoDB
- Redis
- Vector databases such as Pinecone, Weaviate, Qdrant, Chroma or similar
The right person should be able to answer:
What business problem are we solving?
Why is AI required for this problem?
What is the simplest architecture that can solve it?
How will we measure whether the solution works?
Can this become reusable across multiple customers?
What will it cost to operate at scale?
You should be comfortable challenging technical decisions when they do not make business sense and explaining complex technology in simple business terms.
- 9+ years of software engineering experience.
- 5+ years of technical/team leadership experience.
- Strong hands-on experience building production software.
- Experience building and deploying AI-powered applications.
- Practical experience with LLM-based applications and AI agents.
- Strong understanding of system architecture and enterprise integrations.
- Experience with MCP servers or similar AI tool-integration architectures.
- Experience working with cloud platforms and modern DevOps practices.
- Experience working with product, delivery and customer-facing teams.
Experience in SaaS, PropTech, HealthTech, FinTech, enterprise software or other domain-driven products is an advantage.
- Strong ownership and accountability
- Business-first thinking
- Strong architectural judgment
- Ability to simplify complex problems
- High engineering standards
- Practical AI expertise
- Curiosity and continuous learning
- Strong communication
- Ability to challenge assumptions
- Ability to mentor and develop engineers
- Bias toward execution
- Strong customer orientation
In the first 12 months, success will be measured by tangible improvements in:
- Development speed
- Production stability
- Reduction in defects and rework
- Meaningful adoption of AI-assisted development
- Production deployment of AI-powered solutions
- AI engineering standards across teams
- Effective use of AI agents and MCP-based integrations
- Delivery predictability
- Technical risk reduction
- Faster problem resolution
- Improved engineering quality
- Growth of engineering capability
- Stronger technical ownership
- Better mentoring and knowledge sharing
- Stronger technical leadership pipeline
- Better customer outcomes
- Reusable technology and product components
- Improved delivery efficiency
- Contribution to new AI-led business opportunities
Work on AI agents, enterprise automation, LLM applications, MCP and AI-native software products.
You will have an opportunity to influence architecture, engineering practices and AI adoption across the organization.
Work with teams that are increasingly focused on turning customer problems into scalable products and reusable solutions.
Get exposure to enterprise use cases across AI, SaaS, PropTech, HealthTech, FinTech and other technology domains.
The role provides an opportunity to grow beyond engineering management into AI architecture, product strategy and technology leadership.
If you are someone who can understand a business problem, design the right technical solution, build it with modern AI practices, guide engineers through execution and take ownership of the final outcome, this role is for you.
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