Shro Systems

Job Description We are looking for a highly skilled Generative AI Developer with expertise in Large Language Models (LLMs) to join our AI/ML innovation team. The ideal candidate will be responsible for building, fine-tuning, deploying, and optimizing generative AI models to solve complex real-world problems. Responsibilities You will collaborate with data scientists, machine learning engineers, product managers, and software developers to drive forward next-generation AI-powered Responsibilities : - Design and develop AI-powered applications using large language models (LLMs) such as GPT, LLaMA, Mistral, Claude, or similar. - Fine-tune pre-trained LLMs for specific tasks (e.g., text summarization, Q&A systems, chatbots, semantic search). - Build and integrate LLM-based APIs into products and systems. - Optimize inference performance, latency, and throughput of LLMs for deployment at scale. - Conduct prompt engineering and design strategies for prompt optimization and output consistency. - Develop evaluation frameworks to benchmark model quality, response accuracy, safety, and bias. - Manage training data pipelines and ensure data privacy, compliance, and quality standards. - Experiment with open-source LLM frameworks and contribute to internal libraries and tools. - Collaborate with MLOps teams to automate deployment, CI/CD pipelines, and monitoring of LLM solutions. - Stay up to date with state-of-the-art advancements in generative AI, NLP, and foundation Skills Required Skills : - LLMs & Transformers: Deep understanding of transformer-based architectures (e.g., GPT, BERT, T5, LLaMA, Falcon). - Model Training/Fine-Tuning: Hands-on experience with training/fine-tuning large models using libraries such as Hugging Face Transformers, DeepSpeed, LoRA, PEFT. - Prompt Engineering: Expertise in designing, testing, and refining prompts for specific tasks and outcomes. - Python: Strong proficiency in Python with experience in ML and NLP libraries. - Frameworks: Experience with PyTorch, TensorFlow, Hugging Face, LangChain, or similar frameworks. - MLOps: Familiarity with tools like MLflow, Kubeflow, Airflow, or SageMaker for model lifecycle management. - Data Handling: Experience with data pipelines, preprocessing, and working with structured and unstructured Desirable Skills : - Deployment: Knowledge of deploying LLMs on cloud platforms like AWS, GCP, Azure, or edge devices. - Vector Databases: Experience with FAISS, Pinecone, Weaviate, or ChromaDB for semantic search applications. - LLM APIs: Experience integrating with APIs like OpenAI, Cohere, Anthropic, Mistral, etc. - Containerization: Docker, Kubernetes, and cloud-native services for scalable model deployment. - Security & Ethics: Understanding of LLM security, hallucination handling, and responsible AI : - Bachelors or Masters degree in Computer Science, Artificial Intelligence, Machine Learning, or related field. - 2-4 years of experience in ML/NLP roles with at least 12 years specifically focused on generative AI and LLMs. - Prior experience working in a research or product-driven AI team is a plus. - Strong communication skills to explain technical concepts and findings Skills : - Analytical thinker with a passion for solving complex problems. - Team player who thrives in cross-functional settings. - Self-driven, curious, and always eager to learn the latest advancements in AI. - Ability to work independently and deliver high-quality solutions under tight deadlines (ref:hirist.tech)