Applied AI Engineer

About The Role

• We are looking for a Senior AI/ML Engineer who can take AI features from prototype to production with
• confidence. You will own the full lifecycle of our LLM-powered systems — from benchmarking model and
• pipeline performance, to hardening the stack for scale, to shipping it live to real users. This role sits at the
• intersection of applied LLM/GenAI work and MLOps, and is critical to how quickly and reliably we can put new AI
• capabilities in front of customers.
• You will work closely with product, backend, and design to make sure what we ship is fast, accurate, cost-
• efficient, and observable in production.
• What you will do

• Design, build, and ship LLM-powered features end-to-end — including RAG pipelines, agentic workflows,

prompt orchestration, and fine-tuning where it makes sense.

• Define and run benchmarking frameworks for our AI applications: latency, throughput, accuracy,

hallucination rate, cost per request, and quality regressions across model and prompt changes.

• Establish offline evals (golden sets, LLM-as-judge, human-in-the-loop) and online evals (A/B tests,

shadow traffic, canary releases) before any model or prompt goes live.

• Take models and pipelines to production: containerize, deploy, autoscale, and monitor inference

services with clear SLOs for latency, error rate, and cost.

• Build the MLOps backbone — CI/CD for models and prompts, versioning, feature stores where needed,

observability (traces, metrics, logs), and rollback paths.

• Optimize inference performance and cost: batching, caching, quantization, distillation, model routing,

and choosing the right managed vs. self-hosted trade-offs.

• Partner with product to translate fuzzy product asks into measurable AI quality bars, and own the is

this good enough to ship decision with data behind it.

• Mentor other engineers on LLM best practices, eval rigor, and production readiness.

What we are looking for

• 3–6 years of engineering experience, with a meaningful portion spent shipping ML or AI systems to

production (not just notebooks or POCs).

• Strong hands-on experience with LLMs and GenAI: at least one production system using OpenAI /

Anthropic / open-source models, plus practical experience with RAG, embeddings, vector stores, and

prompt engineering.

• Solid MLOps foundation — model serving (FastAPI, vLLM, Triton, SageMaker, or similar),

containerization (Docker, Kubernetes), and at least one cloud (AWS, GCP, or Azure).

• Demonstrated ability to benchmark systems rigorously: you can talk concretely about how you

measured a models quality and performance, what you optimized, and what you knowingly traded off.

• Strong Python skills; comfortable with PyTorch or TensorFlow, and with frameworks like LangChain,

LlamaIndex, or equivalent (or a clear point of view on why not to use them).

• Good engineering discipline: testing, code review, clear API design, and the instinct to add observability

before it is needed.

• Comfortable owning the path to production — you have taken something live, watched it break, and

fixed it, and you do not need a separate team to do that for you.

Bonus points

• Experience fine-tuning or post-training open-source models (LoRA/QLoRA, DPO, RLHF).
• Worked with multimodal models (image, video, or audio generation/understanding).
• Built or contributed to an internal eval harness or LLM observability tooling.
• Experience with high-QPS, low-latency inference at consumer scale.
• Open-source contributions or technical writing in the AI/ML space.

What success looks like in your first 6 months

• You have shipped at least one customer-facing AI feature to production, fully owned by you.
• A benchmarking and evals framework is in place, run on every model or prompt change, with results

visible to the team.

• Production AI services have clear SLOs, dashboards, and alerting — and you can answer what is this

costing us per 1,000 requests without thinking.

• The teams velocity on shipping AI features has measurably increased because of the infrastructure and

  patterns you put in place.

• Why Zocket
• Zocket is building the AI layer for marketing — letting any business create high-performing ads, creatives, and
• campaigns in minutes instead of weeks. AI is not a side project here; it is the product. You will work on systems
• that thousands of businesses use every day, with a tight team, fast feedback loops, and a real mandate to ship.
• How to apply
• Send your resume and links to anything you have shipped (GitHub, projects, papers, demos) to
• [HIDDEN TEXT]. If you have taken an AI system from prototype to production and have a story about how
• you knew it was ready, lead with that.