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 3–5 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: Bachelor’s or Master’s 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.

NLP Engineer