AI / ML Engineer

The core requirements for the job include the following:

Core ML and Data Science:

• Strong foundation in machine learning algorithms (supervised and unsupervised).
• Experience with anomaly detection, time-series, and predictive modelling.
• Proficiency in Python and ML libraries (Scikit-learn, XGBoost, PyTorch/TensorFlow).
• Experience with data preprocessing, feature engineering, and model evaluation.

Generative AI and NLP:

• Hands-on experience with LLMs (OpenAI, Claude, Hugging Face, etc. )
• Strong understanding of RAG (Retrieval-Augmented Generation).

Prompt engineering and evaluation.

• Embeddings and vector databases (e. g., FAISS, Milvus).
• Experience building GenAI applications (chatbots, document summarisation systems).
• Experience working with large datasets (batch + streaming).
• Knowledge of data pipelines and tools (Spark, Airflow, or similar).
• Familiarity with cloud platforms (Azure, AWS, or GCP).

MLOps and Deployment:

• Experience deploying models using APIs (FastAPI/Flask).
• Understanding of Docker, CI/CD pipelines, and model monitoring.
• Knowledge of version control and experiment tracking tools.

Preferred Qualifications:

• Experience in the insurance domain (claims processing, fraud detection, and underwriting analytics).
• Familiarity with document AI / OCR / NLP pipelines for insurance workflows.
• Experience with graph-based or network-based anomaly detection.
• Exposure to multi-agent systems or AI orchestration frameworks.
• Understanding of regulatory and compliance considerations in insurance AI.

Key Use Cases:

• AI-powered claims anomaly and fraud detection systems.
• Intelligent document processing and insights extraction.
• Generative AI-based assistants for claims and underwriting teams.
• Predictive models for risk assessment and customer insights.

Soft Skills:

• Strong problem-solving and analytical thinking.
• Ability to work in a fast-paced, ambiguous environment.
• Effective communication with both technical and business stakeholders.
• Ownership mindset with a focus on delivering production-ready solutions.