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Build AI-powered products and features using LLMs and foundation models
Design and implement RAG (Retrieval-Augmented Generation) systems for production
Build agentic workflows using LangChain, LangGraph, or custom orchestration
Integrate LLM APIs (OpenAI, Anthropic, Gemini, open-source models) into products
Fine-tune models (LoRA, QLoRA) on domain-specific datasets
Build evaluation pipelines to measure AI quality and track regressions
Implement AI safety guardrails, content filtering, and output validation
Python: expert level (async, type hints, packaging, testing, clean architecture)
LLM APIs: OpenAI, Anthropic Claude, Google Gemini, HuggingFace — all fluently
RAG stack: vector databases (Pinecone, Weaviate, Chroma, pgvector)
Embeddings: text-embedding-3, BGE, E5, Cohere Embed models
Prompt engineering: system prompts, few-shot, Chain-of-Thought, structured outputs
LangChain / LlamaIndex / LangGraph for orchestration
Evaluation frameworks: RAGAS, LangSmith, custom eval pipelines
Core LLM integration
LLM application orchestration
Vector database for RAG
Open-source model access
Model and AI API serving
Rapid demo building
RAG and LLM evaluation
Deployment infrastructure
Transformer architecture: attention mechanism, positional encoding (conceptual depth)
Tokenization: BPE, SentencePiece, context window and token count management
Context window management: chunking strategies, overlap, summarization for long docs
RAG evaluation metrics: faithfulness, answer relevancy, context precision, recall
Agent architectures: ReAct, Plan-and-Execute, Multi-Agent systems and their tradeoffs
OpenAI API: build 5 production-like apps (summarizer, Q&A bot, extractor, classifier, chatbot)
RAG from scratch: PyPDF + Chroma + OpenAI Embeddings + GPT-4 — no abstractions first
Prompt engineering patterns: zero-shot, few-shot, CoT, structured JSON outputs
Deploy one AI app publicly (Hugging Face Spaces or Vercel) — make it real
LangChain deep-dive: chains, agents, tools, memory; build a functional ReAct agent
Vector DB comparison: benchmark Pinecone vs Chroma vs pgvector on your use case
RAG evaluation: implement RAGAS metrics; improve faithfulness and relevancy scores
Fine-tuning: LoRA fine-tune Llama 3.1 8B on a domain dataset using Unsloth/PEFT
LangGraph: multi-agent workflows with state machines, cycles, and conditional routing
Production AI stack: FastAPI + Celery + Redis + PostgreSQL + vector DB + monitoring
Open-source LLM deployment: Ollama locally, then vLLM on a cloud GPU instance
Evaluation harness: custom pipeline with regression testing on prompt or model changes
Multi-modal: vision-language models (GPT-4V, LLaVA, Qwen-VL) integration
AI safety: RLHF basics, Constitutional AI concepts, output filtering systems
Build a full AI SaaS product — monetize at even small scale as proof
Target Staff AI Engineer or AI Tech Lead positions
| Level | India | Global | Note |
|---|---|---|---|
| Junior / 0–2 yr | ₹8L – ₹18L | $60K – $100K | LLM API + RAG implementation |
| Mid-level / 3–5 yr | ₹18L – ₹32L | $100K – $155K | Production AI systems + evaluation |
| Senior / 5+ yr | ₹32L – ₹45L | $155K – $200K | AI Tech Lead or Staff AI Engineer |
PDF ingestion + vector search + streaming
LangGraph agentic workflows
LoRA fine-tuning on custom data
GitHub Action + Claude API integration
DeepLearning.AI · Free (audit)
Foundation of LLM app development
Anthropic · Free
Official best practices from Claude creators
DeepLearning.AI · Free (audit)
Production-grade RAG evaluation
AWS · Paid (~$150)
Cloud AI infrastructure validation
Extremely high remote potential. AI Engineer is among the top 3 most in-demand remote roles globally. Indian engineers are directly competitive with global candidates.
Very high demand. Top freelance requests: RAG systems, enterprise chatbots, agentic workflows, AI integration into existing products.
Relying only on LangChain abstractions without understanding what happens underneath
Shipping AI features without evaluation pipelines — this is considered unprofessional
Portfolio with only chatbot demos — show RAG systems, agents, and eval infrastructure
Hottest engineering role in 2025. Will remain top-5 for at least a decade. Foundation model APIs commoditizing — systems thinking, evaluation infrastructure, and agentic design will be the differentiators.
Research, prototype, and productionize ML models — from classical algorithms to deep learning.
View RoadmapDesign, test, and optimize prompts and evaluation pipelines for production LLM applications.
View RoadmapBuild infrastructure for training, deploying, and monitoring ML models in production at scale.
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