Artificial Intelligence Engineer

Key Responsibilities:

Takes Ownership Owns delivery of complex AI-driven product features from design through production. Builds and maintains secure, reliable AI pipelines, APIs, and monitoring capabilities. Troubleshoots and resolves model performance, inference, and deployment issues in production environments. Partners with engineering and product teams to ensure solutions are delivered accurately, consistently, and on schedule. Maintains accountability for system reliability, observability, and long-term maintainability.

Drives Efficiency Improves model accuracy, scalability, latency, and operational performance through disciplined optimization. Designs reusable AI components, services, and frameworks that accelerate development and reduce rework. Implements and refines MLOps workflows to streamline experimentation, testing, and deployment. Uses automation and tooling to increase developer productivity and reduce manual effort. Applies data-driven evaluation and monitoring to ensure continuous improvement.

Innovative Experiments with GenAI and LLM frameworks such as LangChain, Semantic Kernel, and Azure OpenAI. Researches and applies emerging AI techniques to enhance platform intelligence and automation. Uses AI-assisted tools to accelerate prototyping, testing, and iteration. Identifies new opportunities where AI can deliver measurable product and operational impact. Contributes to evolving AI architecture patterns and best practices across the platform.

Customer Centric Designs AI capabilities that improve user experience, operational outcomes, and product value. Incorporates feedback loops, evaluation metrics, and usage data to refine model behavior over time. Ensures AI solutions align with responsible AI standards, security requirements, and compliance expectations. Collaborates with cross-functional partners to ensure AI features solve real customer problems.

Requirements :

5+ years of software engineering experience, with 2–3 years building AI, ML, or LLM-based applications Hands-on experience deploying AI models into production environments Strong proficiency in Python and familiarity with C# Experience with Azure OpenAI, LangChain, Semantic Kernel, FastAPI, and vector databases Experience implementing retrieval-augmented generation (RAG) or vector search systems Strong understanding of prompt engineering, model evaluation, inference optimization, and observability Experience designing and operating AI pipelines, APIs, and monitoring solutions Strong analytical, communication, and collaboration skills