Website:
synaptyx.ai
Job details:
The RoleWe are looking for a Technology Lead who can manage a small engineering team, run tight 10-day sprints, and hold the delivery line without being told to. You'll work across our core solution suite: Lattice, our agentic AI platform and the engine that powers everything we ship. Active client deployments depend on what the team ships each fortnight.
This is a people and delivery role with real technical depth. You will do the architectural calls, the code reviews, and the tricky debugging, alongside the 1:1s, the performance conversations, and the sprint governance that keeps the quarter on track. Being good at one half doesn't substitute for the other. No defined playbook, I'm afraid. If growing a team, shipping working solutions every fortnight, and explaining what you've built to Senior Leadership as fluently as to a fellow engineer is the kind of work that gets you out of bed in the morning, we'd very much like to hear from you.
What You'll Own- Technical Delivery: Architecture decisions, code reviews, and hands-on problem solving across the full product stack. You set the technical standard and hold it.
- Hands-On Leadership: While you’ll be architecting and managing the Tech delivery, you’re also expected to roll up your sleeves and get your hands in the work. That means running AI proof-of-concepts, prototyping art-of-the-possible solutions, and accelerating client pilots and production rollouts.
- Cloud Leadership: Architect and implement solutions across cloud platforms (AWS, Azure, etc.), ensuring flexibility, performance, scalability, and cost-efficiency. Apply Kubernetes and containerisation best practices to deploy and orchestrate cloud-native and GenAI/ AI-ML workloads, with security frameworks across APIs, identity, and access management (SSO, RBAC) built in from the start.
- CI/CD and DevOps: GitHub Actions, pipelines, and the practices that keep deployments consistent and the team moving.
- Sprint Delivery and Governance: Running the FORGE 10-day sprint cycle: Day 1 scoped and designed, Day 10 deployed and demoed. Running agile ceremonies, surfacing blockers before they become a missed sprint, and holding the morning readiness and evening gate review calls. Good work doesn't count until someone can see it run.
- People Management: Line management of the engineering team. Work management and task prioritisation to keep the team focused on sprint goals, alongside 1:1s, career development, and performance conversations.
- Team Mentoring: Not just code reviews, but the kind of input that changes how an engineer approaches a problem.
- Stakeholder Communication: Translating what the team has built into what it does for the client. Without fog.
What You Bring- 3-5 years overall in a technical role, with 1-2 years carrying both delivery and people management accountability.
- 2-4 years of hands-on experience across cloud architecture, deployment, and optimisation. Familiar with Bedrock, Lambda, ECS, EC2, CloudFront, and ALB/NLB on AWS. Azure exposure adds to this.
- Solid grasp of cloud-native design: cost optimisation, security, and performance tuning, and the trade-offs between all three.
- GitHub Actions, CI/CD, and DevOps in practice. Not just on paper.
- Working knowledge of Kubernetes, containerisation, and microservice deployment.
- Strong Python, PySpark, and SQL for building data and AI pipelines.
- Proven track record managing engineers: formal line management including 1:1s, development conversations, and the harder conversations when standards slip.
- Proven experience running delivery: sprint governance, agile ceremonies, and bringing complex technical projects home.
- Communication that holds up in a client room: clear, outcome-focused, and light on jargon.
- A figure-it-out mindset. You make reasonable assumptions, ship something, and iterate. You own what you build and can defend it when challenged. Especially when the client has opinions.
Bonus Points- Industry experience in Finance, Telecom, Retail, or CPG.
- Experience designing or running AI/ML strategies from the ground up.
- Hands-on work building AI agents, agentic orchestration, or LLM/SLM-based workflow automation: MCPs, microservices, token and cost optimisation, model selection, prompt engineering, and context window trade-offs.
- Production experience with agentic or hybrid RAG pipelines.
- Data engineering or analytics background: building analytics-ready datasets, cataloguing, metadata management, and governance frameworks. Exposure to predictive analytics or applied forecasting.
The Mindset We're Hiring ForA figure-it-out mindset. Someone comfortable with ambiguity, proactive in problem solving, and genuinely curious about what's next. You ask the right questions, make reasonable assumptions, ship something, and iterate. You own what you build and can defend it confidently in a client room, even when the client has opinions.
Why SynaptyXYou will not be working on anything that lives in a PowerPoint. SynData, SynSights, SynOps, SynQuest, and Lattice are in client hands and being improved every sprint. Your contributions are visible and attributable. The team is small, senior, and low-ego, built on Big 4 advisory depth and serious AI engineering. You will have the authority that matches the accountability. We move fast. Terrifyingly fast, by most corporate standards.
✅ Do: tell us about the hardest technical trade-off you've made under delivery pressure, and what you'd do differently.
❌ Don't: send "passionate about cloud and AI" with no evidence. We'll assume you mean passionate about the phrase.
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