NLP ক্যারিয়ার রোডম্যাপ

NLP Engineer salary range: $80K (junior) থেকে $500K+ (staff/principal at FAANG)। কিন্তু এই path navigate করা সহজ না। সঠিক skill, সঠিক positioning, সঠিক timing — সব মিলিয়ে career strategy দরকার।

NLP/ML Engineer career path এ মূলত ৩টা track আছে: (1) Research Scientist — PhD/research heavy, paper publish। (2) ML Engineer — production system build, MLOps। (3) Applied AI Engineer — LLM integration, RAG, agent system। প্রতিটার skill set, interview pattern, এবং salary structure আলাদা।

Career = compound interest। প্রথম ২ বছর হয়তো ধীর, কিন্তু সঠিক skill stack করলে ৫ বছরে exponential growth। Key: T-shaped skill — একটা area তে deep expertise (e.g., LLM fine-tuning) + broad knowledge (MLOps, system design, frontend basics)।

• FAANG: Google, Meta, Microsoft, Amazon, Apple — top tier comp।
• AI-first: OpenAI, Anthropic, Cohere, Mistral — frontier work।
• Startups: Hugging Face, LangChain, Weaviate — fast growth।
• Remote-first: Replicate, Together AI — global salary।
• Indie/Solo: SaaS, consulting, YouTube/course creator।

# Career tracking dashboard — track your ML career progress
from dataclasses import dataclass, field
from datetime import date
from typing import List

@dataclass
class Skill:
    name: str
    level: int  # 1-5 (beginner → expert)
    last_practiced: date

@dataclass
class Project:
    name: str
    stack: List[str]
    github_url: str
    deployed: bool
    stars: int = 0

@dataclass
class CareerProfile:
    name: str
    target_role: str  # "ML Engineer" | "Research Scientist" | "Applied AI"
    skills: List[Skill] = field(default_factory=list)
    projects: List[Project] = field(default_factory=list)

    def readiness_score(self) -> dict:
        skill_score = sum(s.level for s in self.skills) / max(len(self.skills), 1)
        project_score = sum(2 if p.deployed else 1 for p in self.projects)
        oss_score = sum(p.stars for p in self.projects) / 100
        total = skill_score * 10 + project_score * 5 + oss_score
        return {
            "skill_avg": round(skill_score, 2),
            "project_score": project_score,
            "oss_score": round(oss_score, 2),
            "total": round(total, 2),
            "readiness": "Ready" if total >= 50 else "Keep building",
        }

# Example
me = CareerProfile(
    name="Future NLP Engineer",
    target_role="Applied AI Engineer",
    skills=[
        Skill("Python", 5, date.today()),
        Skill("PyTorch", 4, date.today()),
        Skill("Transformers", 4, date.today()),
        Skill("FastAPI", 4, date.today()),
        Skill("Docker", 3, date.today()),
        Skill("LLM/RAG", 4, date.today()),
        Skill("Vector DB", 3, date.today()),
    ],
    projects=[
        Project("Bangla Summarizer", ["FastAPI","HF","React"], "github.com/x/y", True, 45),
        Project("RAG Chatbot", ["LangChain","Chroma","Next.js"], "github.com/x/z", True, 120),
    ],
)
print(me.readiness_score())

• শুধু tutorial দেখা, project না বানানো।
• এক জায়গায় apply না করা — fear of rejection।
• Salary negotiate না করা — leave money on table।
• Networking ignore — ৭০% job referral এ হয়।

1. LinkedIn headline + about section optimize করুন (NLP keywords)।
2. GitHub এ ৩-৫টা production-quality project pin করুন।
3. Twitter/X এ daily learning share করুন — build in public।
4. Mock interview করুন (Pramp, Interviewing.io)।

• ৩টা track: Research, ML Engineering, Applied AI — নিজেরটা চিনুন।
• T-shaped skill: deep + broad।
• Portfolio > certificate > degree (often)।
• Networking + build in public = opportunity magnet।
• Negotiate salary — always, even at first offer।