Posted on: 16/10/2025
Description :
- Collaborate with senior ML engineers to design and implement AI product features.
- Train, evaluate, and fine-tune ML and LLM models under guidance.
- Support the development and maintenance of scalable ML pipelines and APIs.
- Assist in data preprocessing, feature engineering, and model evaluation.
- Participate in code reviews, testing, and performance optimization.
- Help deploy and monitor ML models in production environments.
- Learn and apply best practices in LLMOps, DevOps, and cloud deployment.
- Contribute to documentation and internal knowledge-sharing.
- 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 :
- Work on real-world Generative AI and NLP applications for the insurance domain.
- Build and deploy LLM-based pipelines using modern frameworks.
- Gain hands-on experience with cloud ML infrastructure.
- Learn how to manage end-to-end ML workflowsfrom experimentation to deployment.
- Collaborate with cross-functional teams (data, backend, product).
- Contribute to improving model accuracy, reliability, and scalability.
What You Need to Succeed :
- Bachelors or Masters degree in Computer Science, Machine Learning, or related field.
- experience in ML, data science, or related software engineering roles (internships count).
- Strong understanding of ML fundamentals, deep learning architectures, and data processing.
- Experience building or fine-tuning models using Python ML frameworks.
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