Top analysts like Gartner, Forrester, and IDC agree, all showing Agiloft as a leader in the CLM space. Our no code platform is easily managed and administered by business users, which is why Agiloft is the contract you keep: nearly a full 100% of new customers are satisfied with their initial implementations, and some 97% of customers renew every year. Ours is a growing, vibrant, successful company that is at the forefront of a market that is becoming a must-have for all organizations.
We believe that the way to build the strongest, most vibrant place to work is to bring in individuals from all walks of life, and to support them in bringing their authentic selves to their day, every day. Our working philosophy is that “EX = CX”: when employee experience is excellent, so is customer experience. We support multiple Employee Resource Groups (ERGs), and offer a working environment that supports healthy work/life balance, including floating holidays and a quarterly, no-questions-asked wellness day.
Position Overview
As a Sr. AI Engineer, you will play a key role in designing, building, and maintaining AI-powered enterprise applications. You will work closely with product managers, designers, and engineers to deliver innovative software solutions that solve high-stakes problems for our customers. Your primary focus will be on Screens, an AI-powered contract review application, where you will apply cutting-edge techniques using LLMs, RAG, and AI agent frameworks. You will also contribute to improving internal AI tooling, driving best practices, and mentoring team members.
Job Responsibilities
- Design, build, and maintain AI-driven enterprise applications, with a focus on integrating and optimizing commercial and open source LLM APIs
- Evaluate and implement RAG pipelines, agent architectures, and fine-tuned model solutions
- Partner with product managers and designers to identify AI opportunities, brainstorm solutions, and deliver intuitive, high-quality user experiences
- Develop and maintain robust backend services and APIs to support AI-powered features, ensuring scalability and reliability
- Continuously improve AI model evaluation processes and system performance, focusing on accuracy, latency, and cost-efficiency
- Enhance internal AI developer tools, workflows, and infrastructure to increase productivity and streamline AI experimentation
- Lead efforts to establish AI engineering best practices, including prompt engineering, evaluation frameworks, and error handling for AI systems
- Troubleshoot and resolve complex issues across the AI and application stack, ensuring high availability and fault tolerance
- Participate in incident response, disaster recovery efforts, and ongoing system monitoring
- Other duties as assigned
Required Qualifications
- Bachelor’s degree in Computer Science, Information Technology, or a related field (or equivalent experience)
- Minimum 5+ years of professional experience as a software engineer, with 3+ years of applied AI/ML experience
- Proficiency in using relevant AI Tools in the SLDC (for example, GitHub Copilot, JetBrains AI Assistant).
- Proven experience integrating commercial LLM APIs (OpenAI, Anthropic, or similar) into modern software applications
- Strong knowledge of building cloud-native applications using serverless technologies (API Gateway, Lambda, DynamoDB or similar)
- Deep understanding of AI model evaluation, prompt engineering, and RAG techniques
- Strong problem-solving skills with a solid understanding of system architecture, distributed systems, and performance optimization
- Familiarity with AI agent frameworks, evaluation methodologies, and benchmarking tools
- Hands-on experience building RAG-based systems, working with vector databases, and AI evaluation pipelines
- Excellent collaboration, teamwork, and communication skills
- Eagerness to stay up to date with the latest AI advancements
Preferred Qualifications
- Exposure to cutting-edge agentic frameworks, LLM chaining, or other complex LLM workflows
- Experience with fine-tuning commercial or open source LLMs
- Proficiency in Infrastructure as Code with AWS (CloudFormation, SAM, or Terraform)
- Hands-on experience with traditional data science and machine learning techniques
- Comfortability with data science tooling in Python (numpy, scipy, scikit-learn, pandas) to support AI experimentation
- Experience with CI/CD pipelines and DevOps practices using GitHub Actions or similar
- Knowledge of containerization and orchestration using Docker and ECS
Applications will be reviewed as submitted. There will be no application deadline for this opportunity.
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