GenAI Engineer

Role Overview:

Soul AI is seeking a talented Generative AI Engineer to join our client's team. In this role, you will be instrumental in building intelligent systems powered by large language models (LLMs) and other generative AI architectures. Your responsibilities will include developing and deploying LLM-based features, integrating vector search capabilities, fine-tuning models, and collaborating closely with product and engineering teams to deliver robust, scalable GenAI applications.

You will work across the entire GenAI stack, from prompt design to inference optimization, shaping the real-world application of generative models.

Responsibilities:

• Fine-tune and deploy LLMs (e.g., GPT, LLaMA, Mistral) using frameworks such as Hugging Face Transformers or LangChain.
• Build and optimize Retrieval-Augmented Generation (RAG) pipelines utilizing vector databases (e.g., Pinecone, FAISS).
• Engineer prompts for structured, reliable outputs across various use cases including chatbots, summarization, and coding copilots.
• Implement scalable inference pipelines and optimize latency, throughput, and cost through techniques like quantization or model distillation.
• Collaborate with product, design, and frontend teams to seamlessly integrate GenAI into user-facing features.
• Monitor, evaluate, and continuously improve model performance, safety, and accuracy in production environments.
• Ensure compliance with privacy, safety, and responsible AI practices, including content filtering and output sanitization.

Required Skills:

• Strong programming skills in Python, with familiarity in modern ML tooling.
• Practical experience with LLM frameworks like Hugging Face Transformers, LangChain, or LlamaIndex.
• Experience building or deploying RAG pipelines, including handling embeddings and vector search.
• Understanding of transformer models, prompt engineering, and tokenization strategies.
• Hands-on experience with APIs (OpenAI, Anthropic, Cohere, etc.) and model serving frameworks (FastAPI, Flask, etc.).
• Experience deploying ML models using Docker, Kubernetes, and/or cloud services (AWS/GCP/Azure).
• Comfortable with model evaluation, monitoring, and troubleshooting inference pipelines.

Nice to Have:

• Experience with multimodal models (e.g., diffusion models, TTS, image/video generation).
• Knowledge of RLHF, safety alignment, or model fine-tuning best practices.
• Familiarity with open-source LLMs (e.g., Mistral, LLaMA, Falcon, Mixtral) and optimization techniques (LoRA, quantization).
• Experience with LangChain agents, tool usage, and memory management.
• Contributions to open-source GenAI projects or published demos/blogs on generative AI.
• Exposure to frontend technologies (React/Next.js) for prototyping GenAI tools.

Educational Qualifications:

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related technical field. Candidates with relevant project experience or open-source contributions may be considered regardless of formal degree.