Ayush Singh

The AI-Native Engineering Sprint

by Antern Data Solutions

An 18-week, implementation-first cohort that turns working professionals into AI-native engineers — through production systems, live builds, research thinking, and outreach engineering.

📅 18 Weeks · 👥 100 Seats per Cohort · 🧱 7 Curriculum Blocks · 🛤 5 Parallel Rails · 🚀 Cohort 2 orientation: August 1, 2026

Outcomes are contextual. Antern does not guarantee a job, salary, offer, or placement.

The Real Problem

You're a working engineer or ML practitioner, and you can feel AI catching up fast. The gap between "uses AI tools" and "builds AI systems that survive production" is widening — and the market will eventually force the transition on you whether you're ready or not.

This isn't a skills-checklist problem. It's a depth, proof, and positioning problem.

Depth: You can call an API, but can you design the system around the model — loops, evaluation, retries, guardrails, cost control — when the environment is noisy and adversarial?

Proof: Your GitHub has tutorial projects. Nothing signals that you can defend an architecture decision or explain why a system failed under real constraints.

Positioning: You're waiting for opportunity instead of engineering it. Good AI-native roles fill through networks, proof-of-work, and direct outreach — not job boards.

The Philosophy

Not another course. A system for producing AI-native engineers. Distribution over skill alone. Proof-of-work over credentials. Systems, product judgment, reasoning, and execution — not just models.

Who Should Not Apply

Absolute beginners, passive learners, people looking for a shortcut, or anyone who wants AI to replace their thinking instead of amplifying it.

Target Roles Across the Cohort

Calibrated against what frontier labs, FDE teams, AI product teams, infrastructure labs, and seed-to-Series-B AI-native startups actually screen for: Applied AI Engineer, Forward Deployed Engineer, Agent Engineer, AI SWE, AI Infrastructure Engineer, AI Product Engineer, Research Engineer, and AI Startup Engineer.

The Curriculum Spine — 7 Blocks

Block A · Weeks 1–4 Math + ML Foundations. Probability, statistics, information theory, linear algebra, optimization, classical ML, deep learning, model evaluation, and engineering judgment.

Block B · Weeks 5–7 Transformers + LLM Internals. Attention, GPT internals, RoPE, KV cache, MoE, inference, quantization, LoRA/QLoRA, batching, and serving tradeoffs.

Block C · Weeks 8–10 AI Systems + Backend + Data Engineering. GPU architecture, Flash Attention, FastAPI, async systems, Postgres, Redis, queues, Airflow, Kafka, embedding pipelines, and vector database internals.

Block D · Weeks 11–13 LLM Engineering + RAG + Agents. Context engineering, structured outputs, memory systems, retrieval science, GraphRAG, tool calling, agent loops, HITL, and durable execution.

Block E · Weeks 14–15 Evaluation + Reliability + Security. Golden datasets, slice evaluation, LLM-as-judge, regression gates, tracing, cost dashboards, guardrails, prompt-injection defense, and red-teaming.

Block F · Weeks 16–17 RLHF + Agentic RL + Frontier Research. Policy gradients, reward modeling, RLHF, DPO, GRPO, RLVR, reasoning models, test-time compute, diffusion, VAEs, and research taste.

Block G · Week 18 Production Capstone + Hiring Sprint. A production AI system with architecture decision records, an evaluation harness, deployment, monitoring, cost tracking, a live demo, and public proof-of-work.

Five Rails That Run All 18 Weeks

AI Coding Systems (coding agents, AI IDE workflows, patch verification) · Paper Club (read, critique, reproduce, defend) · Failure Friday (real AI failures: hallucination, prompt injection, RAG failure, reward hacking) · Engineering Judgment (defend a real technical decision every week) · Startup Operator + Outreach Engineering (ship, talk to users, publish, and create opportunity systematically).

The Weekly Learning Loop

Productive Failure → Retrieval Warmup → Theory + Live Demonstration → Guided Build → Independent Build → Self-Explanation → Proof of Work. If you can't explain the tradeoffs, alternatives, and failure modes, the work isn't done.

Hard Constraint Labs

Serious agents don't run in clean tutorial environments. You'll build under partial information, ambiguous intent, policy-constrained tool use, token and latency budgets, and evaluators that punish inconsistent behavior. The question isn't whether a model can answer once — it's whether a system behaves reliably when the environment is constrained and adversarial.

How Outcomes Work

Outcomes are produced by systems, not promises. The program builds the conditions that make opportunity easier to justify — capability, visible proof, positioning, communication, and systematic outreach — then treats opportunity creation as an engineering problem (ICP selection, prospect research, messaging, follow-ups, CRM hygiene, campaign debugging).

Outcomes evidence is kept in a counsellor-mediated vault with masked participant identities, showing pipeline activity, positive conversations, and meetings. It is read as cohort pipeline evidence, not as a placement promise. Individual results depend on your effort, communication, domain choice, portfolio quality, interview readiness, follow-through, and current market conditions.

What Antern Does Not Claim

Antern does not guarantee jobs, salaries, offers, internships, founder introductions, or meetings. Credibility matters more than hype.

Instructor & Network

Led by Ayush Singh, who teaches AI engineering through research, implementation, business reality, and operator-level judgment. The network layer comes from Antern, SecondBrain Labs, public teaching, business operations, and counsellor-mediated introductions.

Cohort Shape

• 18 weeks · Advanced level · 100 seats per cohort · Cohort 2 orientation August 1, 2026. The public page shows curriculum architecture; exact weekly topics, papers, projects, and evaluation rubrics are shared through the Antern counsellor flow.