Website:
grovebybanyan.com
Job details:
TechGrove is the Centre of Excellence for Banyan Software, based in Chennai, India. It plays a key role in supporting Banyan’s global businesses through technology, security, and software development. TechGrove brings together India’s deep pool of technical talent with Banyan’s long-term approach to growth, creating a trusted, developer-focused environment where people can do their best work.
About SubItUpSubItUp is a leading provider of cloud-based scheduling solutions that empower organizations to optimize resource management, enhance team productivity, and streamline operations. We believe that everyone can love what they do for work. We help teams — from global corporations to small businesses — solve their problems through a collection of workforce tools that enable them to work more efficiently. In the end, our platform fuels their success, improvement, and smiles.
Our innovation supports universities, government agencies, and businesses in achieving operational excellence through cutting-edge technology. At SubItUp, we constantly strive for excellence. Every iteration is an opportunity for evaluation and improvement. We are a team dedicated to achieving desired outcomes together in a kind, exciting, and forward-moving way.
The RoleWe are hiring a senior, hands-on Principal AI Engineer – Acceleration & Innovation to accelerate engineering performance and drive AI-enabled innovation across SubItUp in our workflows and in our SaaS product.
This is a high-impact individual contributor role. You will not manage people. You will not run sprint ceremonies. You will not replace the Technical Lead.
You will:
- Increase engineering leverage through structured AI adoption
- Systematize how AI is used across the engineering team – engineering AI tooling, vector DBs, agentic coding, MCP adoption & creation, etc.
- Rapidly execute strategic and tactical technical initiatives
- Serve as a direct technical partner to the CPTO
- Lead strategy, architect & implementation of leveraged AI systems in our product
Your mandate is simple: materially improve engineering throughput and capability using AI.
Success in this role will be measured by substantial improvements in engineering throughput and a shift toward AI-enabled, systems-level thinking across the team and secondarily, the product.
Why This Role ExistsSubItUp has strong architectural leadership and engineering management in place.
What we need now is someone who can:
- Turn AI from a personal productivity tool into a team multiplier
- Standardize and improve AI-assisted engineering workflows
- Eliminate duplicated effort through disciplined practices
- Execute both complex and fast-moving initiatives
- Stay ahead of AI advancements so the organization evolves with the technology
This role focuses on acceleration, execution, and practical innovation.
Core Responsibilities1. AI Adoption & Engineering Workflow Acceleration (Primary Responsibility)You are accountable for measurable improvement in engineering velocity and code quality through disciplined AI usage.
This is not about experimentation for its own sake. It is about building repeatable, high-leverage practice.
You will:
- Lead structured adoption of Claude Code, Claude Cowork, Copilot, Cursor, and emerging AI platforms
- Define standards for prompt design, context management, and responsible AI-assisted development
- Develop reusable prompt patterns for common engineering tasks (debugging, refactoring, testing, documentation, API design)
- Establish shared AI project workspaces where appropriate to reduce duplicated effort
- Create lightweight libraries of prompts, templates, and AI playbooks
- Integrate AI into real workflows such as code review preparation, test generation, documentation, and onboarding
- Improve consistency and quality of AI-assisted outputs
- Evaluate new AI techniques and adopt only what delivers practical value
The goal is to move the team from ad hoc AI usage to structured, compounding leverage.
2. Rapid Technical ExecutionYou will execute a range of initiatives, including:
- Smaller, fast-moving internal projects requiring strong AI and coding fluency
- Automation and internal tooling improvements
- Focused enhancements to existing systems
- Prototypes and proof-of-concepts
- High-impact, technically ambiguous initiatives
Some projects will be architecturally deep.
Some will simply need to be built quickly and correctly.
You must be effective in both modes.
3. AI-Enabled Product Innovation- Partner with Product to design AI-enabled capabilities
- Translate LLM capabilities into scalable, production-ready implementations
- Understand model constraints, latency, cost, reliability, and security considerations
- Ensure AI features are durable, scalable and aligned with overall system architecture
4. Strategic Technical Partnership- Serve as a direct engineering partner to the CPTO
- Identify high-leverage opportunities across the platform
- Provide forward-looking insight into AI feasibility and direction
- Support architectural evolution without displacing existing technical leadership
Required Experience- 10+ years of software engineering experience
- Proven experience designing and scaling production systems
- Demonstrated success improving team productivity through structured AI-assisted development practices
- Experience improving engineering workflows or developer productivity
- Strong bias toward execution and measurable outcomes
Technical Environment Some experience with our tech stack is preferred but not required. Architectural experience is required.
- C#, .NET (Core / 5+), Azure
- Modern front-end frameworks (React preferred)
- RESTful API design
- SQL Server and distributed data systems
- CI/CD pipelines, Docker
- Plus some old code that needs some serious attention
- Strong automated testing discipline
AI Fluency (Mandatory)- Expert daily use of Claude, Copilot, Cursor, or equivalent
- Advanced prompt construction and context management
- Experience integrating LLM-powered functionality into applications
- Practical understanding of AI limitations, evaluation methods, and cost tradeoffs
- Ability to standardize AI usage across a team environment
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