AI Engineer

AI Engineers build production systems on top of large language models — RAG search, agents, fine-tuned models, prompt-orchestration pipelines, and the evals and guardrails that keep them reliable. The work sits between traditional software engineering and applied ML: you write Python services, design retrieval pipelines (vector search, BM25, rerankers), pick or fine-tune the right base model, write evals that quantify quality, and ship features against latency and cost SLOs. In India through 2026, the role is one of the fastest-growing technical hires — concentrated at fintechs (Razorpay, Cred, Paytm), SaaS companies (Freshworks, Zoho, Postman, Atlan, Hasura), AI-native startups (Sarvam AI, Krutrim, Ola Krutrim, Yellow.ai), and the GCCs of Microsoft, Google, Adobe, and Salesforce — and pays a clear premium over generalist backend roles.

• r/LocalLLaMA
• r/LangChain
• BharatGen / AI4Bharat Discord
• Sarvam community
• Hugging Face India
• MLOps Community Slack
• Builders' Asylum

• Designing Machine Learning SystemsBook
  by Chip Huyen
  The definitive engineering-side ML book — eval design, feature stores, monitoring, deployment trade-offs. Most of it transfers cleanly to LLM engineering.
• Sebastian Raschka's substackBlog
  by Sebastian Raschka
  Clean, technical write-ups on fine-tuning, LoRA, and LLM internals — strong signal-to-noise ratio compared to most AI Twitter.
• Lil'LogBlog
  by Lilian Weng
  Long-form deep dives into agents, RLHF, hallucination taxonomies, and prompt engineering. Read it twice — most AI engineers stop too early.
• Deep Learning with Python (2nd ed.)Book
  by François Chollet
  Strong fundamentals foundation — even AI engineers who never train a model from scratch benefit from understanding what's inside the API call.
• Let's build GPT (and the full Zero-to-Hero series)YouTube
  by Andrej Karpathy
  The single best resource for understanding what a transformer actually does. Watch all of it; do the exercises.
• Eugene Yan's blogBlog
  by Eugene Yan
  Pragmatic essays on evals, RAG patterns, and applied-LLM lessons from production teams. Cited often in interviews.
• Latent Space podcastPodcast
  by swyx and Alessio Fanelli
  Long-form interviews with AI engineers and founders shipping in production. Best way to absorb the working vocabulary of the field.
• Hamel Husain's blogBlog
  by Hamel Husain
  Eval-first writing from an engineer who consults with serious AI teams. The 'evaluating LLM apps' series is required reading.

• Write or refactor a RAG retrieval pipeline — embedding choice, chunking strategy, reranker, prompt template — and run the eval set against the change to measure quality lift.
• Investigate a hallucination report from product or support: reproduce the failure, isolate whether it's a retrieval miss, a prompt issue, or a model-capability gap, and draft the fix.
• Review 2-3 PRs from teammates: check prompt diff, eval-set additions, latency and token-cost impact, and graceful-degradation logic when the model API fails.
• Run an eval batch on a new model (e.g., Claude vs GPT vs an open-weight Llama variant) — analyze quality, latency, and per-1K-token cost, then write a 1-page recommendation memo.
• Pair with a backend or DE teammate on the data layer: add a new field to the retrieval index, debug a slow vector query, or design the schema for a new tool the agent will call.
• Attend a 15-30 min daily standup and 1-2 ad-hoc syncs (with PM, designer, or model-policy reviewer) about a new AI feature, eval results, or a customer-reported quality issue.