NLP / Conversational AI Engineer

💬

Inference Specialization

NLP engineers build language understanding, generation, and conversation systems. The most mature AI engineering discipline — now turbocharged by LLM integration. Consistently the third most-posted AI engineering role globally.

Days

Projects

$175K

Median

Most-Posted AI Role

$130K–$230K

Salary Range

Week 1: NLP Foundations and Advanced Transformers · Days 1–8  

Deep-dive on NLP architectures, supervised fine-tuning strategies, and production semantic search systems.

D1–3

Classical NLP pipeline: tokenisation, NER, dependency parsing

The full stack: POS tagging, named entity recognition, coreference, dependency. Foundation for LLM context

↗ NLP Specialization — DeepLearning.AI/Coursera (free audit)

D4–5

BERT, RoBERTa, DeBERTa, and domain adaptation

Encoder models for classification, NER, and Q&A. Fine-tuning strategies and domain-specific vocabulary

↗ HuggingFace NLP Course Chapter 3 (free)

D6–7

Semantic search: bi-encoders, cross-encoders, ColBERT

Dense retrieval, sentence embeddings, two-stage retrieval and re-ranking for state-of-the-art search

↗ Sentence Transformers official documentation (free)

🏗 Project — Day D8: Domain-Specific NER System and REST API 

· 500 or more annotated sentences using Label Studio

· Fine-tuned BERT-NER model above 0.85 F1 on test set

· Active learning loop for efficient annotation

· FastAPI inference endpoint with sub-100ms latency

· Error analysis report with confusion categories

Week 2: Conversational AI and Dialogue Systems · Days 9–24  

Build production-grade dialogue systems: intent classification, slot filling, LLM-powered conversation management, voice.

D9–12

Dialogue management: intent, slots, and multi-turn state

Task-oriented dialogue architecture. Intent classification, slot filling, state tracking, conversation recovery

↗ Rasa Open Source documentation (free)

D13–16

LLM-powered dialogue: function calling and guardrails

Replacing rule-based NLU with LLMs. Function calling for actions, guardrails for reliability

↗ OpenAI Function Calling guide (free)

D17–20

Voice AI: Whisper, Deepgram, ElevenLabs, and voice UX

ASR, TTS, voice interface design principles, and latency optimisation for real-time voice assistants

↗ HuggingFace Audio Course (free)

D21–23

Content moderation and text classification at scale

Hierarchical classification, active learning, and LLM-based moderation for high-volume text streams

↗ HuggingFace Text Classification guide (free)

🏗 Project — Day D24: Production Customer Service Bot with Voice 

· Intent classifier with over 95% accuracy across 20 intents

· Slot filling covering 5 customer service scenarios

· LLM response generation with guardrails layer

· Voice interface: Whisper ASR and ElevenLabs TTS

· Escalation-to-human logic with context handoff

Week 3: Information Extraction, Search, and Enterprise NLP · Days 25–40  

Relation extraction, document summarisation at scale, hybrid neural search, and multilingual capability.

D25–28

Information extraction: events, relations, knowledge graphs

Relation extraction, event detection, coreference resolution, building knowledge graphs from text

↗ Stanford CS224N NLP — YouTube slides and notes (free)

D29–32

Summarisation: extractive, abstractive, multi-document

BART, T5, and LLM-based summarisation for long documents, meeting notes, and news clustering

↗ HuggingFace Summarisation task guide (free)

D33–36

Hybrid neural search: BM25 plus dense retrieval

Elasticsearch neural plugin, ColBERT for production semantic search, hybrid scoring at scale

↗ Elasticsearch semantic search guide (free)

D37–39

Multilingual NLP and low-resource languages

mBERT, XLM-RoBERTa, language-agnostic sentence embeddings, fine-tuning on low-resource corpora

↗ HuggingFace Multilingual NLP guide (free)

🏗 Project — Day D40: Enterprise NLP Platform — Full Capstone 

· Document classification and entity extraction pipeline

· Semantic search with bi-encoder plus cross-encoder re-ranking

· Conversational interface with voice input and output

· Content moderation pipeline with thresholds

· Unified evaluation report across all components