Hands-on AI and software engineering challenges

I spent 3+ hours in the last 2 weeks putting together this no-nonsense curriculum so you can break into AI as a software engineer in 2025. This post (plus flowchart) gives you the latest AI trends, core skills, and tool stack you’ll need. I want to see how you use this to level up. Save it, share it, and take action. ➦ 1. LLMs (Large Language Models) This is the core of almost every AI product right now. think ChatGPT, Claude, Gemini. To be valuable here, you need to: →Design great prompts (zero-shot, CoT, role-based) →Fine-tune models (LoRA, QLoRA, PEFT, this is how you adapt LLMs for your use case) →Understand embeddings for smarter search and context →Master function calling (hooking models up to tools/APIs in your stack) →Handle hallucinations (trust me, this is a must in prod) Tools: OpenAI GPT-4o, Claude, Gemini, Hugging Face Transformers, Cohere ➦ 2. RAG (Retrieval-Augmented Generation) This is the backbone of every AI assistant/chatbot that needs to answer questions with real data (not just model memory). Key skills: -Chunking & indexing docs for vector DBs -Building smart search/retrieval pipelines -Injecting context on the fly (dynamic context) -Multi-source data retrieval (APIs, files, web scraping) -Prompt engineering for grounded, truthful responses Tools: FAISS, Pinecone, LangChain, Weaviate, ChromaDB, Haystack ➦ 3. Agentic AI & AI Agents Forget single bots. The future is teams of agents coordinating to get stuff done, think automated research, scheduling, or workflows. What to learn: -Agent design (planner/executor/researcher roles) -Long-term memory (episodic, context tracking) -Multi-agent communication & messaging -Feedback loops (self-improvement, error handling) -Tool orchestration (using APIs, CRMs, plugins) Tools: CrewAI, LangGraph, AgentOps, FlowiseAI, Superagent, ReAct Framework ➦ 4. AI Engineer You need to be able to ship, not just prototype. Get good at: -Designing & orchestrating AI workflows (combine LLMs + tools + memory) -Deploying models and managing versions -Securing API access & gateway management -CI/CD for AI (test, deploy, monitor) -Cost and latency optimization in prod -Responsible AI (privacy, explainability, fairness) Tools: Docker, FastAPI, Hugging Face Hub, Vercel, LangSmith, OpenAI API, Cloudflare Workers, GitHub Copilot ➦ 5. ML Engineer Old-school but essential. AI teams always need: -Data cleaning & feature engineering -Classical ML (XGBoost, SVM, Trees) -Deep learning (TensorFlow, PyTorch) -Model evaluation & cross-validation -Hyperparameter optimization -MLOps (tracking, deployment, experiment logging) -Scaling on cloud Tools: scikit-learn, TensorFlow, PyTorch, MLflow, Vertex AI, Apache Airflow, DVC, Kubeflow

If you’re preparing for AI Engineer interviews, here’s a practical step-by-step cheatsheet that can help. End-to-end system design approach Here’s the breakdown: 𝟭. 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 & 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 • Clean, version, and prepare datasets for consumption • Think SQL, Pandas, Spark/Ray ~ garbage in, garbage out; also look into DVC for data versioning. 𝟮. 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 • Build and debug model architectures • PyTorch is the industry standard (TensorFlow is legacy but useful) ~ know how to write custom training loops and loss functions. 𝟯. 𝗟𝗟𝗠𝘀 & 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 • Control model behavior via context and instructions • Chain-of-Thought, Few-Shot prompting, and ReAct patterns ~ learn framework abstractions like LangChain or DSPy. 𝟰. 𝗥𝗔𝗚 (𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻) • Connect LLMs to private/external data sources • Vector DBs (Pinecone/Milvus), Embeddings, and Chunking strategies ~ context window management is key. 𝟱. 𝗠𝗟𝗢𝗽𝘀 & 𝗖𝗜/𝗖𝗗 𝗳𝗼𝗿 𝗠𝗟 • Automate training, testing, and deployment workflows • Model Registry (MLflow/Weights & Biases) + feature stores = reproducible AI. 𝟲. 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 • Adapt foundation models to specific domains efficiently • PEFT, LoRA, QLoRA ~ full fine-tuning is rarely necessary anymore; know when to prompt vs. when to tune. 𝟳. 𝗠𝗼𝗱𝗲𝗹 𝗦𝗲𝗿𝘃𝗶𝗻𝗴 & 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 • Expose models as scalable APIs with low latency • FastAPI, Triton Inference Server, vLLM ~ handling concurrent requests and batching strategies. 𝟴. 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 & 𝗤𝘂𝗮𝗻𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻 (𝗙𝗶𝗻𝗢𝗽𝘀 𝗳𝗼𝗿 𝗔𝗜) • Reduce model size and compute costs without losing quality • FP16 vs INT8, Pruning, Distillation ~ running big models on smaller GPUs saves money. 𝟵. 𝗔𝗜 𝗦𝗮𝗳𝗲𝘁𝘆 & 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 • Measure performance and prevent harmful outputs • RAGAS for RAG eval, Guardrails, and Hallucination detection ~ accuracy metrics (F1/Recall) aren't enough for GenAI. 𝗛𝗼𝘄 𝘁𝗼 𝘀𝘁𝘂𝗱𝘆 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 (𝗺𝘆 𝗮𝗱𝘃𝗶𝗰𝗲): → Understand the math, but master the implementation → Learn how to debug a model (it’s harder than debugging code) → Always ask: "Do we need an LLM for this, or will a simple regression work?" This isn’t an exhaustive list ~ but you should also look into topics like AI Agents (Tool Use), Multi-modal models, GPU Architecture, and Edge AI. What else would you add that should be covered? Found this post valuable? reshare! Follow me (Priyanka) for more visual AI and Cloud learnings #ai #aiagents #aiengineering

If I had to move from software engineering to Applied AI Engineer and had only 90 days to prepare, this is how I would structure my prep. Days 1-30: Foundations LLM Fundamentals ↳ How LLMs work: training, inference, tokenization, context windows ↳ Where they fail: hallucinations, context limits, reasoning gaps ↳ When to use RAG vs fine-tuning vs prompt engineering Retrieval-Augmented Generation ↳ Chunking strategies: recursive, semantic, sentence, parent-child ↳ Embedding models: selection, tradeoffs, benchmarking ↳ Vector search: similarity metrics, indexing, metadata filtering ↳ Reranking: cross-encoders, why this is a high-leverage retrieval component Evaluation ↳ Why this matters: AI systems are non-deterministic, you can't test them like APIs ↳ Retrieval metrics: precision, recall, MRR, NDCG ↳ Generation metrics: faithfulness, relevance, groundedness ↳ LLM-as-judge: building automated evaluation pipelines ↳ Golden datasets: curating ground truth for your domain Days 31-60: Build Project 1 — Production RAG Pipeline ↳ Your domain, your data, end to end ↳ Hybrid search (sparse + dense) ↳ Reranking ↳ Evaluation framework with measured baselines ↳ Every architectural decision backed by an experiment Project 2 — AI Agent with Tool Use ↳ Multi-step reasoning with real tools (APIs, databases, code execution) ↳ Error handling and recovery ↳ Agent trace evaluation ↳ Cost and latency tracking Production Layers ↳ Caching, routing, observability ↳ Deployment (FastAPI, Docker, cloud) ↳ Monitoring: drift detection, latency, cost per query Days 61-90: Interview Prep Coding ↳ DSA and system design prep (still required) ↳ AI engineering rounds need you to build real systems in the interview ↳ Anthropic: implement an in-memory database across 4 progressive levels, passing tests to unlock each ↳ OpenAI: refactor code to production quality, ↳ Prepare for both: algorithms for screens, practical engineering for onsites System Design (AI-specific) ↳ RAG pipeline for large-scale document search ↳ Multi-agent system architecture ↳ LLM serving at scale: batching, sharding, GPU memory ↳ Practice explaining tradeoffs out loud Project Defense ↳ Walk through every decision in your projects ↳ Know your failure modes ↳ Quantify everything: latency, accuracy, cost ↳ "I chose X over Y because my evaluation showed Z" Portfolio Positioning ↳ GitHub with clean READMEs and architecture diagrams ↳ Quantified metrics in every project description ↳ Production signals: error handling, monitoring, deployment ↳ Show evaluation discipline, this is the differentiator Skills that matter most in 2026: Tier 1: RAG, Agents, Evaluation Tier 2: Fine-tuning, Production Infrastructure, Safety Tier 3: Context Engineering, Multi-modal, MCP The loop that transfers to every AI system: Assemble Context → Generate → Evaluate → Improve. ___ 👋 P.S. This is exactly what the Engineer's RAG Accelerator covers in 6 structured weeks. To join the April cohort → [Visit my website]

After reviewing 300+ AI/ML resumes, here's what actually gets people hired 👇 (I'm an AI/ML Engineering Manager and these patterns separate successful candidates from those who struggle) 🔹 𝗪𝗵𝗮𝘁 𝗱𝗼𝗲𝘀𝗻'𝘁 𝘄𝗼𝗿𝗸 𝗮𝗻𝘆𝗺𝗼𝗿𝗲: → 15 toy projects with MNIST datasets → Memorizing every new AI paper without implementation → Perfect theoretical knowledge but can't deploy anything → Chasing every framework instead of mastering fundamentals → Building "Hello World" chatbots and calling it RAG 🔹 𝗪𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗴𝗲𝘁𝘀 𝘆𝗼𝘂 𝗵𝗶𝗿𝗲𝗱: 𝗦𝗼𝗹𝘃𝗲 𝗿𝗲𝗮𝗹 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 → Build a document Q&A system for legal contracts → Create a resume screening bot that actually works in production → Design systems that handle edge cases and failures gracefully 𝗘𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 → Show you can take messy data → clean model → production deployment → monitoring → Demonstrate cost-latency tradeoffs, not just accuracy metrics → Prove you understand when NOT to use LLMs 𝗦𝘁𝗿𝗼𝗻𝗴 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 + 𝗖𝗼𝗱𝗶𝗻𝗴 𝘀𝗸𝗶𝗹𝗹𝘀 → I'd rather hire someone who deeply understands linear regression than someone who can name 50 algorithms → Can you implement cosine similarity from scratch? → Debug a broken RAG pipeline? 🔹 𝗥𝗲𝗱 𝗳𝗹𝗮𝗴𝘀 𝗜 𝘀𝗲𝗲 𝗼𝗳𝘁𝗲𝗻: 𝗙𝗼𝗿 𝗝𝘂𝗻𝗶𝗼𝗿 𝗖𝗮𝗻𝗱𝗶𝗱𝗮𝘁𝗲𝘀: → Can't explain their own projects clearly → No deployed demos or GitHub links → Theory without practical application → Weak coding fundamentals 𝗙𝗼𝗿 𝗦𝗲𝗻𝗶𝗼𝗿 𝗖𝗮𝗻𝗱𝗶𝗱𝗮𝘁𝗲𝘀: → No system design thinking or scalability awareness → Can't discuss cost-performance tradeoffs → Haven't dealt with real production issues → Focus on trends over engineering judgment 🔹 𝗠𝘆 𝗮𝗱𝘃𝗶𝗰𝗲 𝗯𝘆 𝗹𝗲𝘃𝗲𝗹: 𝗝𝘂𝗻𝗶𝗼𝗿 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 (𝟬-𝟮 𝘆𝗲𝗮𝗿𝘀): → Focus on 80/20: Coding fundamentals, 2-3 deployed projects, basic system design → Don't try to master everything - depth beats breadth → Build something users actually interact with 𝗠𝗶𝗱-𝗹𝗲𝘃𝗲𝗹 (𝟮-𝟱 𝘆𝗲𝗮𝗿𝘀): → Show you can own features end-to-end → Demonstrate debugging skills and production experience → Understand when to use which tools and why 𝗦𝗲𝗻𝗶𝗼𝗿+ (𝟱+ 𝘆𝗲𝗮𝗿𝘀): → We're looking for engineering judgment and system thinking → Can you design systems that scale and handle edge cases? → Do you mentor others and drive technical decisions? The reality: The bar is higher than ever, but opportunities are massive for those who prepare strategically. Consistency beats intensity - daily practice with real problems trumps cramming. 💭 Comment your thoughts below 👇 -- ♻️ Repost if you think its helpful 👍 ➕ Follow me, Shantanu for production AI/ML/MLOps & careers ➕ Join 21.000+ AI/ML builders here: https://lnkd.in/ds_SzEUH