Recognized as one of the most promising emerging players in the insurance technology space, Gigaforce has already achieved significant milestones. We were a finalist for InsurtechNY, a leading platform accelerating innovation in the insurance industry, and twice named a Top 50 company by the TiE Silicon Valley community. Additionally, Plug and Play Tech Center, the worlds largest early-stage investor and innovation accelerator, selected Gigaforce to join its prestigious global accelerator headquartered in Sunnyvale, California.

At the core of our platform is a commitment to cutting-edge innovation. We harness the power of technologies such as AI, Machine Learning, Robotic Process Automation, Blockchain, Big Data, and Cloud Computingleveraging modern languages and frameworks like Java, Kotlin, Angular, and Node.js.

We are driven by a culture of curiosity, excellence, and inclusion. At Gigaforce, we hire top talent and provide an environment where every voice matters and every idea is valued. Our employees enjoy comprehensive medical benefits, equity participation, meal cards and generous paid time off. As an equal opportunity employer, we are proud to foster a diverse, equitable, and inclusive workplace that empowers all team members to thrive.

Job Description Were seeking a NLP & Generative AI Engineers with 2-8 years of hands-on experience in traditional machine learning, natural language processing, and modern generative AI techniques. If you have experience deploying GenAI solutions to production, working with open-source technologies, and handling document-centric pipelines, this is the role for you.

Youll work in a high-impact role, leading the design, development, and deployment of innovative AI/ML solutions for insurance claims processing and beyond.

Responsibilities In this agile environment, you'll work within structured sprints and leverage data-driven insights and user feedback to guide decision-making. You'll balance strategic vision with tactical execution to ensure we continue to lead the industry in subrogation automation and claims optimization for the property and casualty insurance Responsibilities :

• Build and deploy end-to-end NLP and GenAI-driven products focused on document understanding, summarization, classification, and retrieval.
• Design and implement models leveraging LLMs (e.g., GPT, T5, BERT) with capabilities like fine-tuning, instruction tuning, and prompt engineering.
• Work on scalable, cloud-based pipelines for training, serving, and monitoring models.
• Handle unstructured data from insurance-related documents such as claims, legal texts, and contracts.
• Collaborate cross-functionally with data scientists, ML engineers, product managers, and developers.
• Utilize and contribute to open-source tools and frameworks in the ML ecosystem.
• Deploy production-ready solutions using MLOps practices : Docker, Kubernetes, Airflow, MLflow, etc.
• Work on distributed/cloud systems (AWS, GCP, or Azure) with GPU-accelerated workflows.
• Evaluate and experiment with open-source LLMs and embeddings models (e.g., LangChain, Haystack, LlamaIndex, HuggingFace).
• Champion best practices in model validation, reproducibility, and responsible Skills & Qualifications :
• 2-8 years of experience as a Data Scientist, NLP Engineer, or ML Engineer.
• Strong grasp of traditional ML algorithms (SVMs, gradient boosting, etc.) and NLP fundamentals (word embeddings, topic modeling, text classification).
• Proven expertise in modern NLP & GenAI models, including :
• Transformer architectures (e.g., BERT, GPT, T5)
• Generative tasks : summarization, QA, chatbots, etc.
• Fine-tuning & prompt engineering for LLMs
• Experience with cloud platforms (especially AWS SageMaker, GCP, or Azure ML).
• Strong coding skills in Python, with libraries like Hugging Face, PyTorch, TensorFlow, Scikit-learn.
• Experience with open-source frameworks (LangChain, LlamaIndex, Haystack) preferred.
• Experience in document processing pipelines and understanding structured/unstructured insurance documents is a big plus.
• Familiar with MLOps tools such as MLflow, DVC, FastAPI, Docker, KubeFlow, Airflow.
• Familiarity with distributed computing and large-scale data processing (Spark, Hadoop, Qualifications :
• Experience deploying GenAI models in production environments.
• Contributions to open-source projects in ML/NLP/LLM space.
• Background in insurance, legal, or financial domain involving text-heavy workflows.
• Strong understanding of data privacy, ethical AI, and responsible model usage.

(ref:hirist.tech)