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Job Description Job Summary We are seeking an experienced Natural Language Processing (NLP) Engineer with strong Generative AI expertise to lead the design, development, and deployment of advanced language intelligence solutions. The ideal candidate will bring deep expertise in core NLP techniques, transformer architectures, and language understanding, along with hands-on experience in large language models (LLMs), prompt engineering, model fine-tuning, reasoning techniques, evaluation frameworks, and agentic AI systems. This role will be instrumental in creating intelligent agents and AI-powered products that deliver measurable business impact. Key Responsibilities Design and implement advanced prompting strategies for large language models to achieve consistent and reliable outputs at scale. Develop and deploy NLP solutions including text classification, named entity recognition, sentiment analysis, summarization, and question answering systems. Work with transformer-based architectures (BERT, GPT, T5, LLaMA, etc.) for building and customizing language models. Lead fine-tuning initiatives for LLMs and transformer models, including data preparation, training pipeline development, and model optimization. Develop and maintain robust evaluation frameworks to measure model performance, accuracy, and alignment with business objectives. Architect and build agentic AI systems that can autonomously plan, reason, and execute complex multi-step workflows. Design AI agents with capabilities including tool calling, function execution, memory management, and self-correction mechanisms. Required Qualifications Experience: Minimum 8 years in NLP with at least 35 years of hands-on experience working with large language models and transformer-based applications in production environments. NLP Expertise: Strong foundation in natural language processing concepts including text preprocessing, tokenization, embeddings, named entity recognition, sentiment analysis, and text generation. Transformer Models: In-depth understanding of transformer architectures (attention mechanisms, encoder-decoder models) and hands-on experience with models like BERT, GPT, T5, RoBERTa, LLaMA, or similar. Prompt Engineering: Proven expertise in designing, testing, and optimizing prompts for LLM applications at scale. Fine-Tuning: Demonstrated experience in fine-tuning foundation models using techniques such as LoRA, QLoRA, GRPO. Reasoning & Evaluation: Strong background in implementing reasoning frameworks (CoT, ReAct, etc.) and building evaluation pipelines to assess model quality. Agentic AI: Hands-on experience in designing, building, and deploying AI agents with capabilities including autonomous task execution, tool use, planning, and decision-making. Additional Requirements Agent Architectures: Experience with single-agent and multi-agent systems, agent orchestration, memory management, and agent-to-agent communication patterns. Agent Frameworks: Working knowledge of agent development frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or similar tools. Programming: Strong proficiency in Python and familiarity with ML frameworks such as PyTorch, TensorFlow, or JAX and NLP libraries like Hugging Face Transformers, spaCy, or NLTK. Education: Bachelors or Masters degree in Computer Science, Machine Learning, Data Science, or a related field. Preferred Qualifications Experience with RAG (Retrieval-Augmented Generation) architectures and vector databases. Hands-on experience building production-grade agentic applications with complex workflows and error handling. Experience with agent evaluation, debugging, and monitoring in production environments. Knowledge of MLOps practices, model deployment pipelines, and cloud platforms (AWS, GCP, Azure). Experience working with open-source LLMs and commercial APIs (OpenAI, Vertex AI, Anthropic, Cohere). Familiarity with agent memory systems (short-term, long-term, episodic memory) and context management strategies. .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.