Ai Ml Engineer

Job Summary

We are looking for a passionate AI Engineer with strong expertise in Python, Natural Language Processing (NLP), and Large Language Models (LLMs). Youll work on developing intelligent language-based solutions, integrating LLM APIs (like OpenAI, Cohere, Claude, Hugging Face), and building custom NLP pipelines for real-world applications.

Key Responsibilities
Model Development & NLP Engineering

• Design and build NLP pipelines for tasks like classification, NER, summarization, sentiment analysis, and Q&A.
• Fine-tune and optimize open-source LLMs (e.g., BERT, GPT, T5) using Hugging Face Transformers.
• Preprocess and structure large-scale textual datasets using SpaCy, NLTK, or custom tokenizers.

LLM Integration & Prompt Engineering

• Integrate LLM APIs (OpenAI, Claude, Cohere, Hugging Face) into real-time applications.
• Develop and iterate on prompt strategies to maximize LLM performance and reliability.
• Collaborate with product teams to embed LLMs into chatbots, match explainers, recommendation systems, and user-facing tools.

Deployment & Continuous Improvement

• Deploy models as APIs using Flask, FastAPI, or Streamlit; containerize with Docker.
• Monitor model outputs and performance metrics; iterate based on evaluations and user feedback.
• Stay updated with the latest in GenAI, NLP benchmarks, and open-source tools.

Required Skills & Experience

• Proficient in Python and common NLP libraries (SpaCy, Transformers, NLTK, Gensim).
• Experience integrating LLM APIs and building prompt-driven applications.
• Hands-on with Hugging Face Transformers for training/fine-tuning.
• Understanding of key NLP concepts: embeddings, attention, tokenization**.**
• Familiarity with vector DBs like FAISS, Weaviate, or Pinecone (for RAG).
• Comfortable with Git, containerization (Docker), and basic REST API development.

Preferred Bonus Skills

• Experience with LangChain, LlamaIndex, PromptLayer, or similar LLMOps tools.
• Knowledge of RAG workflows, zero/few-shot techniques.
• Familiarity with evaluation metrics like BLEU, ROUGE, perplexity, cosine similarity.
• Exposure to AWS, GCP, or Azure for cloud deployment.
• Contributions to NLP open-source projects or research work is a plus.