AI & Data Engineer

Overview

Align Labs is building an AI-first marketing OS for modern teams—turning messy, multi-channel marketing into measurable growth. We’re a SaaS + microservices platform that helps brands plan, create, ship, and optimize campaigns across channels (WhatsApp, email, ads, web) using LLMs, automation, and a strong data backbone.

We’re looking for an AI & Data Engineer who’s also a software engineer and builder at heart—someone who can design and ship real-world AI systems that blend LLMs + data engineering + product infrastructure, and cares about latency, reliability, and measurable outcomes (CTR, CAC, ROAS, retention).

What you’ll do

• Build, deploy, and scale LLM-powered marketing workflows:️—brief → creative generation → compliance checks → multi-variant rollout → learning loop.
• Design brand-safe generation systems (tone, claims, policy constraints) using guardrails, evals, and structured prompting.
• Implement RAG pipelines for “brand + product + audience + past performance” memory (embeddings, hybrid search, vector DBs).
• Ship agents + tool-calling for marketing ops: campaign setup, audience segmentation, offer testing, reporting, and automated insights.
• Engineer data pipelines (batch + streaming) for events + attribution: ad platforms, web analytics, CRM, WhatsApp/campaign logs.
• Run offline/online experiments (A/B, multi-armed bandits, holdouts) to improve conversion, cost efficiency, and personalization.
• Build internal AI tooling for observability + evaluation: prompt/version tracking, hallucination checks, safety, and regression dashboards.
• Work across product + backend to productionize systems: APIs, queues, caching, rate limits, cost controls, and SLAs.

What we’re looking for

• Strong foundations in CS / ML / Data Engineering (degree or equivalent experience).
• Proven ability to ship production AI systems end-to-end (not just notebooks).
• Hands-on with modern AI tooling: PyTorch / Hugging Face, LangChain/LlamaIndex (optional), OpenAI-compatible APIs.
• Solid grasp of LLM patterns: prompting, structured outputs, finetuning (where needed), inference optimization, eval-driven iteration.
• Strong backend skills: Python/Node, APIs, queues (SQS/Kafka), caching (Redis), Postgres, and cloud deployment (AWS).
• Comfort with vector DBs + retrieval systems and agent orchestration patterns.
• Builder mindset: lean, fast, pragmatic—ships MVPs that scale into real systems.

Bonus points for

• Experience with marketing/ads data: attribution, incrementality, MMM/MTA basics, ROAS/CAC modeling.
• Experience with personalization/recommendation systems.
• Experience with low-latency inference + cost optimization (batching, caching, distillation, quantization).
• Open-source contributions, demos, or shipped products people actually use.
• Experience building for India (WhatsApp-first flows, multilingual audiences, tier-2/3 realities).

Why Align Labs?

• You’ll build the “brain” for marketing teams—AI that actually moves metrics, not just generates copy.
• Direct ownership: design → ship → measure → iterate, with freedom to experiment.
• Real distribution problems: multi-channel delivery, brand safety, attribution, and learning loops at scale.
• A lean team, fast shipping culture, and a product that can become the default marketing OS for emerging markets.

Next steps
Share: (1) Projects you shipped end-to-end, (2) what you built + what metrics improved, (3) links to GitHub/demos (if any) to build@alignlabs.xyz