GenAI Engineer

Join Soul AI as a Generative AI Engineer and contribute to building intelligent systems powered by cutting-edge large language models and generative AI architectures. This role involves developing and deploying LLM-based features, integrating vector search capabilities, fine-tuning models, and collaborating closely with product and engineering teams to launch robust and 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.

Key Responsibilities:

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

Required Skills:

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

Nice to Have:

• Experience with multimodal models, including diffusion models, text-to-speech (TTS), and image/video generation.
• Knowledge of Reinforcement Learning from Human Feedback (RLHF), safety alignment, or advanced model fine-tuning techniques.
• Familiarity with open-source LLMs (e.g., Mistral, LLaMA, Falcon, Mixtral) and optimization methods like LoRA and quantization.
• Experience with LangChain agents, tool usage, and memory management.
• Contributions to open-source GenAI projects or published content (demos, blogs) related to generative AI.
• Exposure to frontend technologies like React or 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 significant open-source contributions will be considered irrespective of formal degree qualifications.