Senior AI Engineer - NLP & Generative AI

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About the Role

We are seeking a highly motivated Senior AI Engineer with a strong background in Natural Language Processing (NLP) and Generative AI to join our growing team. The ideal candidate will be passionate about advancing open-source LLMs, embedding techniques, and vector databases, and will have hands-on experience building classification models. Experience in the customer support industry and knowledge of MLOps/LLMOps practices will be a strong plus.

Key Responsibilities

Research, design, and implement state-of-the-art NLP and Generative AI models.
Fine-tune and evaluate open-source Large Language Models (LLMs) for domain-specific tasks.
Develop and optimize embedding techniques for information retrieval, classification, and RAG (Retrieval-Augmented Generation).
Integrate and scale vector databases for semantic search and knowledge management.
Build and deploy NLP classification models for real-world use cases, including intent detection, sentiment analysis, and text classification.
Collaborate with engineering and product teams to operationalize AI solutions in production environments.
Contribute to research publications, technical blogs, and open-source projects.
Leverage MLOps/LLMOps practices to ensure smooth training, deployment, and monitoring of AI models.

Benefits

Hybrid setup
Worker's insurance
Paid Time Offs
Other employee benefits to be discussed by our Talent Acquisition team in India.

Helpshift embraces diversity. We are proud to be an equal opportunity workplace and do not discriminate on the basis of sex, race, color, age, sexual orientation, gender identity, religion, national origin, citizenship, marital status, veteran status, or disability status

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Experience & Requirements

5+ years of experience in software engineering with at least 3+ years in AI/ML systems.
Strong proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow).
Solid understanding of NLP algorithms, transformer architectures, and language model training/fine-tuning.
Experience with open-source LLMs (e.g., LLaMA, Mistral, Falcon, etc.).
Expertise in embedding techniques (e.g., OpenAI, HuggingFace, SentenceTransformers) and their applications in semantic search and RAG.
Experience with vector databases such as Weaviate, Pinecone, FAISS, or Milvus.
Hands-on experience building NLP classification models.
Familiarity with MLOps tools (e.g., MLflow, DVC, Kubeflow) and LLMOps platforms for managing LLM pipelines.
Excellent problem-solving skills, ability to work in a fast-paced environment.

Preferred Qualifications

Prior experience in the customer support or conversational AI industry.
Knowledge of deploying AI applications in cloud environments (AWS, GCP, Azure).
Experience with prompt engineering and fine-tuning for specific downstream tasks.