Senior Software Engineer (Generative AI / Agentic AI)

About The Role

As a Senior Software Engineer, you will make an impact by designing, developing, and deploying innovative Generative AI and Agentic AI solutions that solve complex business challenges. You will be a valued member of the engineering team and work collaboratively with architects, data scientists, product owners, and cross-functional stakeholders to deliver scalable AI-powered applications.

Responsibilities

• Design and implement single-agent and multi-agent AI systems using frameworks such as LangChain, Semantic Kernel, CrewAI, AutoGen, or similar technologies.
• Develop and deploy applications leveraging large language models (LLMs) including Azure OpenAI, OpenAI, Anthropic, and related platforms.
• Build and optimize Retrieval-Augmented Generation (RAG) pipelines to improve response accuracy and contextual relevance.
• Enable collaboration and orchestration across multiple AI agents within complex agent ecosystems.
• Create secure, scalable APIs, microservices, and distributed systems that support AI-driven solutions.
• Create evaluation frameworks to measure agent performance, including accuracy, response quality, and hallucination detection.
• Maintain and continuously improve system reliability and operational efficiency.
• Optimize AI solutions for performance, scalability, latency, and cost effectiveness.

Requirements

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field.
• 3–8 years of experience in software engineering, application development, or data engineering.
• Hands-on experience building Generative AI or LLM-powered applications in enterprise environments.
• Experience developing APIs, microservices, or distributed systems using modern software engineering practices.
• Strong understanding of AI/ML concepts, prompt engineering, and LLM integration patterns.
• Experience implementing RAG architectures and integrating external data sources with language models.
• Proficiency in programming languages commonly used for AI application development, such as Python.
• Experience developing secure, scalable, and production-ready applications.

Qualifications

• Experience working with agentic AI frameworks such as LangChain, Semantic Kernel, CrewAI, AutoGen, or similar platforms.
• Experience deploying AI solutions on cloud platforms, particularly Microsoft Azure.
• Knowledge of AI model evaluation techniques, observability frameworks, and responsible AI practices.
• Familiarity with vector databases, embeddings, and semantic search technologies.
• Experience optimizing AI workloads for performance, scalability, and operational costs.