AI Engineer

About this role

Design, build, and deploy production AI solutions across the full development lifecycle—from prototyping and model integration to deployment and continuous improvement. You'll deliver agentic AI, voice agents, chatbots, and intelligent automation systems for organizations across healthcare, professional services, private equity, and other industries.

Requirements

• 3–5 years of professional experience in AI/ML engineering or a closely related software engineering role
• Strong proficiency in Python with hands-on experience building and deploying ML/AI pipelines
• Experience working with large language models (LLMs), prompt engineering, RAG architectures, and conversational AI frameworks
• Familiarity with agentic AI patterns including tool use, multi-step reasoning, and autonomous task execution
• Production experience with cloud platforms (AWS preferred) including services such as Lambda, SageMaker, Bedrock, or equivalent
• Solid understanding of REST APIs, microservices architecture, and system integration patterns
• Experience with TypeScript/JavaScript for full-stack AI application development
• Familiarity with vector databases (Pinecone, Weaviate, pgvector) and embedding-based retrieval systems
• Experience with CI/CD pipelines, containerization (Docker), and infrastructure-as-code practices
• Strong written and verbal communication skills with the ability to explain technical concepts to non-technical stakeholders

Responsibilities

• Design and implement AI solutions including voice agents, chatbots, agentic workflows, and intelligent automation systems for client engagements
• Build and integrate LLM-powered applications using frameworks such as LangChain, LlamaIndex, or custom orchestration layers
• Develop and optimize RAG pipelines, prompt chains, and multi-agent architectures for enterprise use cases
• Collaborate with solutions architects and client stakeholders to translate business requirements into technical specifications
• Deploy and monitor AI systems in production environments, ensuring reliability, scalability, and performance
• Implement data pipelines for ingestion, transformation, and preparation of training and inference data
• Conduct code reviews, write technical documentation, and contribute to internal engineering standards
• Stay current with rapidly evolving AI technologies, tools, and best practices and contribute to team knowledge sharing
• Participate in client-facing technical discussions, demos, and solution walkthroughs as needed