Job Description
Who we are
80% of the workers across the globe are Deskless. These are the people who keep our lights on and gas flowing, build roads and bridges, run our manufacturing factories, ensure that we get healthcare service, and provide us with reliable phone and internet connectivity. As entrepreneurs, have we considered solving their problems and making them more productive?
Zinier is a company on a mission to empower frontline workers — and the people supporting them — to achieve greater things for themselves and the world around them. With the majority of workers worldwide being deskless, Zinier recognizes the need for Technology Equity to improve the lives and productivity of these workers who keep the world up and running.
We are a remote-first, global team headquartered in Silicon Valley, with a hybrid workforce across the United States, Canada, Europe, Latin America, Singapore, and Bangalore, India, with leading investors that include Accel, ICONIQ Capital, Founders Fund, Newfund Capital, NGP Capital, Tiger Global Management, and Qualcomm Ventures LLC.
What we are looking for
Do you light up when you see a messy, manual workflow and immediately start thinking about how to automate it — not with the fanciest tool, but with the right one? Are you the kind of engineer who instinctively knows when a rule engine will do the job and when you genuinely need a multi-agent AI system? Zinier is searching for a hands-on, deeply technical Senior AI Engineer who wants to build the AI capability that powers every function across the company.
In this high-impact role, you’ll work closely with the AI Lead Architect to design, build, deploy, and enable solutions enterprise-wide — across Solution Delivery, Product, Platform Engineering, Customer Success, Sales, Operations, and Finance. Leadership defines the problems worth solving. You determine the fastest, most cost-effective way to solve them — whether that’s a data consolidation layer, a visibility dashboard, a workflow automation, or a purpose-built AI agent.
This isn’t a research role. You’ll be shipping working prototypes in your first 30 days, iterating based on real usage, and hardening solutions for production. Every implementation must be justified on cost, complexity, and value. A table when a table suffices. An agent when an agent is genuinely needed. Deep AI expertise isn’t here to make things complex — it’s here to ensure you always pick the simplest, most effective approach.
Bring your builder mentality, your deep understanding of how generative AI actually works under the hood, and your instinct for pragmatic engineering. This is your opportunity to define how an entire company works with AI.
Where you are located
Based in India, Bangalore, working closely with the AI Lead Architect and collaborating with cross-functional teams across departments and time zones as needed.
What the role offers
- Design pragmatic solutions for real problems — assess each use case and select the right approach: data aggregation, visibility tooling, rule engines, workflow automation, or AI/ML. Not every problem requires AI; every solution requires justification.
- Rapid prototyping and iterative delivery — ship functional prototypes within days, validate value with real users, and iterate or kill based on outcomes. The first solution may not be the most optimal, but it will provide the much needed speed to build.
- Build agentic AI systems where justified — design and implement multi-agent architectures, autonomous workflows, and LLM-based tooling when the use case warrants the complexity and cost.
- Score and prioritise opportunities — standardise evaluation of use cases using impact, feasibility, data readiness, and time-to-value frameworks.
- Transition prototypes to production — partner with Platform Engineering to deploy validated solutions as scalable, monitored, production-grade systems.
- Build reusable infrastructure — create shared systems, templates, and patterns that enable departments to independently build and extend AI solutions over time.
- Synthesise findings into recommendations — deliver structured proposals with clear problem statements, technical approaches, projected outcomes, and success criteria.
- Justify cost, complexity, and value — document implementation economics for every solution, enabling leadership to make informed investment decisions. UI/UX is a critical consideration: deliver the right interface for the problem, not unnecessary complexity.
- Train and enable departmental teams — conduct hands-on sessions covering solution functionality, prompt engineering, usage patterns, and troubleshooting. Develop user guides, SOPs, and playbooks for all deployed solutions.
- Cultivate AI champions across the org — identify and develop power users in each department who become first-line support and internal advocates for AI adoption.
- Elevate company-wide AI literacy — help teams across the organisation understand what AI can and can’t do, how to use it effectively, and how to think about it as a tool in their daily work.
What you’ll bring to the role
- Deep understanding of AI/ML fundamentals — neural network architectures, generative AI mechanics, transformer models, and how these systems work at a technical level. This depth is essential for making sound build-vs-buy and AI-vs-simpler-approach decisions.
- Proficiency in at least one modern programming language with the ability to build solutions, automations, and data pipelines efficiently
- Deep working knowledge of Claude (including Claude Code CLI) — prompt architecture, tool-use patterns, and LLM-native application development
- Demonstrated experience building agentic AI systems: multi-agent orchestration, inter-agent handoffs, tool integration, and autonomous workflows
- Comprehensive understanding of token economics: context window optimization, cost management, rate limit handling, and efficiency-aware solution design
- Proven rapid prototyping ability with disciplined, hypothesis-driven experimentation and iterative development
- Strong cross-functional communication — deeply curious about how businesses operate; ability to translate between technical and non-technical audiences, deliver training, and present to leadership
- 3+ years software engineering experience with substantive focus on AI/ML, LLM applications, or automation engineering
- Experience with multi-agent frameworks (CrewAI, AutoGen, LangGraph) or equivalent custom agent architectures is a strong plus
- Familiarity with agent harness patterns: tool registration, context management, memory systems, and orchestration layers
- Experience with automation platforms (n8n, Zapier, Make) and API-first integration development is a plus
- Working knowledge of RAG architectures, vector databases (Pinecone, Weaviate, ChromaDB), and embedding-based retrieval
- Domain experience in FSM, enterprise SaaS, or workforce management; MCP server development; JavaScript/TypeScript; analytics platforms (QuickSight, Tableau, Looker) and SQL are all a plus
- First-principles problem solver: strong technical judgement with the ability to make pragmatic decisions, balancing long-term scalability with near-term delivery
- Hustler mentality with engineering craft: resourceful, persistent, and pragmatic; you ship working systems, not just designs. Comfortable navigating ambiguity in a fast-paced environment
What good looks like
Day 30: Embedded across functions. First two tools shipped. Backlog live.
Day 60: Three or more tools in active use. Teams trained in partnership with SMEs. Playbook draft complete.
Day 90: Quarter roadmap presented. Impact metrics established. Recommendation on scaling the function.
Be Hungry. Be Humble. Be Honest. And Hustle.
Own the AI capability. Build the solutions. Be the reason our teams and customers thrive.
