Senior AI Developer

Requirements

• Minimum 10 years of software development experience.
• At least 3 years of hands-on experience in AI/ML and Generative AI.
• Strong programming skills in Python.
• Experience with AI/ML frameworks such as TensorFlow, PyTorch, and scikit-learn.
• Hands-on experience with Large Language Models (LLMs) and Generative AI technologies such as OpenAI or Azure OpenAI.
• Experience deploying AI solutions on cloud platforms (Azure, AWS, or GCP).
• Strong understanding of data engineering, ETL pipelines, and database technologies (SQL/NoSQL).
• Experience with API development and integration (REST, FastAPI, etc.).
• Excellent communication and stakeholder management skills.
• Ability to work independently with minimal supervision.
• Bachelor’s degree in Computer Science, Engineering, or related field (Master’s preferred).
Preferred Skills
• Experience in insurance or financial services domains.
• Knowledge of LLM frameworks such as LangChain, Langfuse, or similar tools.
• Experience with Prompt Engineering and Retrieval-Augmented Generation (RAG).
• Hands-on experience with Azure services such as Azure OpenAI, Azure ML, and Cognitive Services.
• Understanding of containerization technologies (Docker, Kubernetes) and CI/CD pipelines.
• Exposure to Infrastructure as Code tools such as Bicep.

Responsibilities

AI Solution Development
• Design, develop, and deploy AI and Machine Learning models for enterprise applications.
• Build and implement Generative AI solutions using Large Language Models (LLMs).
• Develop and integrate AI-powered APIs and microservices using frameworks such as FastAPI or REST APIs.
Project & Technical Leadership
• Lead technical discussions with IT partners, vendors, and internal stakeholders.
• Manage end-to-end AI project lifecycles, from concept and design to deployment and optimization.
• Collaborate with cross-functional teams to translate business requirements into technical AI solutions.
AI Architecture & Innovation
• Evaluate and recommend AI technologies, tools, and platforms based on business requirements.
• Conduct proof-of-concepts (POCs) and technical evaluations of AI/ML platforms.
• Document technical architectures, design decisions, and implementation guidelines.
MLOps & Performance Optimization
• Implement best practices for MLOps, model monitoring, and performance optimization.
• Ensure models are scalable, reliable, and production-ready in enterprise environments.
• Work with data engineering pipelines and ETL processes for AI model training and deployment