Artificial Intelligence Engineer

Key Responsibilities:

• Design, implement, and deploy AI agents and generative AI solutions using Python, with hands-on use of LangChain and related orchestration frameworks
• Build modular, scalable, and reusable agent components, including memory, planning, tool use, retrieval, and multi-modal capabilities
• Develop retrieval-augmented generation (RAG) solutions using embeddings, vector databases, and enterprise data sources
• Integrate open-source and proprietary large language models and evaluate the appropriate model for each business use case
• Collaborate with Research, Managed Services, product, data, and technology teams to translate business needs into practical AI solutions
• Prototype and evaluate agent behaviors, prompt strategies, learning approaches, and tool-calling workflows
• Build and maintain scalable data pipelines and APIs that support AI development, evaluation, deployment, and monitoring
• Benchmark solution performance, identify bottlenecks, and implement improvements for quality, reliability, latency, and cost
• Implement observability, testing, evaluation, guardrails, and monitoring to support reliable and responsible AI delivery
• Maintain CI/CD and MLOps/LLMOps practices for controlled and repeatable delivery of AI capabilities
• Follow data governance, security, privacy, and intellectual property requirements throughout the solution lifecycle
• Communicate technical concepts clearly to both technical and non-technical stakeholders and contribute to knowledge sharing

Key Skills:

• Bachelor's degree in computer science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or a related field; advanced degree preferred
• 5 to 8 years of relevant experience in AI/ML engineering, software engineering, data science, or a related technical field
• Demonstrated hands-on experience implementing AI solutions using LangChain. Experience should include building and deploying working solutions, not only theoretical knowledge
• Strong programming experience in Python and experience developing APIs, reusable services, and data pipelines
• Hands-on knowledge of agentic AI patterns, RAG, prompt engineering, embeddings, vector databases, model evaluation, and tool calling
• Experience with one or more cloud platforms such as Azure, AWS, or GCP and containerization technologies such as Docker or Kubernetes
• Experience with CI/CD, MLOps, or LLMOps practices, including testing, deployment, monitoring, and controlled releases
• Ability to explain technical decisions, solve ambiguous business problems, and collaborate effectively with cross-functional stakeholders
• Ownership mindset, curiosity, proactive problem solving, and a commitment to continuous learning and collaboration
• Fluency in English and any additional language relevant to the working market, where applicable