Pibit.ai is transforming the underwriting landscape with Generative AI.

Our SaaS solutions help insurance companies in the US make smarter, faster decisions by optimizing underwriting processes, reducing risk, and improving premiums.

As we expand, were looking for a Machine Learning Engineer 2 to build NLP, CV & LLM based solutions and generate value for our customers.

Position Overview :

As a MLE-2, you will design, implement, and optimize AI solutions while ensuring model success.

You will lead the ML lifecycle from development to deployment, collaborate with cross-functional teams, and enhance AI capabilities to drive innovation and impact.

Key Responsibilities :

- Design and implement AI product features.

- Maintain and optimize existing AI systems.

- Train, evaluate, deploy, and monitor ML models.

- Design ML pipelines for experiment, model, and feature management.

- Implement A/B testing and scalable model inferencing APIs.

- Optimize GPU architectures, parallel training, and fine-tune models for improved performance.

- Deploy LLM solutions tailored to specific use cases.

- Ensure DevOps and LLMOps best practices using Kubernetes, Docker, and orchestration frameworks.

Technical Requirements :

LLM & ML: Hugging Face OSS LLMs, GPT, Gemini, Claude, Mixtral, Llama.

LLMOps: MLFlow, Langchain, Langgraph, LangFlow, Langfuse, LlamaIndex, SageMaker, AWS Bedrock, Azure AI.

Databases: MongoDB, PostgreSQL, Pinecone, ChromDB Cloud: AWS, Azure DevOps: Kubernetes, Docker.

Languages: Python, SQL, JavaScript.

Certifications (Bonus): AWS Professional Solution Architect, AWS Machine Learning Specialty, Azure Solutions Architect Expert.

What You'll Do :

- Collaborate with cross-functional teams to design and build scalable ML solutions.

- Implement state-of-the-art ML techniques, including NLP, Generative AI, RAG, and Transformer architectures.

- Deploy and monitor ML models for high performance and reliability.

- Innovate through research, staying ahead of industry trends.

- Build scalable data pipelines following best practices.

- Present key insights and drive decision-making.

What You Need to Succeed :

- Masters degree or equivalent experience in Machine Learning.

- 3+ years of industry experience in ML, software engineering, and data engineering.

- Proficiency in Python, PyTorch, TensorFlow, and Scikit-learn.

- Strong programming skills in Python and JavaScript.

- Hands-on experience with ML Ops practices.

- Ability to work with research and product teams.

- Excellent problem-solving skills and a track record of innovation.

- Passion for learning and applying the latest technological advancements.

Why Join Us ?

- Work directly with experienced founders.

- Be part of a high-energy team that works hard and celebrates success.

- Shape the future of AI-driven automation in the insurance industry.