AI Engineer

• Design and build agentic AI systems that facilitate conversational workflows for seller enablement
• Develop a chat-based seller assistant that:
• Answers seller questions using enterprise data
• Provides insights on ordering, pricing, and customer behavior
• Generates pre-visit one-pagers to guide sales conversations
• Integrate large language models (e.g., Claude, ChatGPT) with internal data sources and APIs
• Apply prompt engineering, context engineering, and agent orchestration techniques to improve reliability and usefulness
• Build and evolve the platform using LangChain and LangGraph (initially in Python)
• Design persistence and data access layers using Mongo or other data stores
• Collaborate closely with product, analytics, and engineering partners to shape both technical direction and use cases
• Serve as a champion for modern AI coding assistant tools (e.g., Cursor) within the team
• Help define standards and best practices as this AI capability scales

Machine Learning Engineer

• Monitor, maintain, and optimize the agentic infrastructure running on LangChain / LangSmith / LangGraph
• Manage container-based deployments and ensure stability across environments
• Build and own CI/CD pipelines for agent and model deployments
• Set up and maintain observability — tracing, alerting, and performance dashboards for LLM-based systems
• Support MCP server integration as the client-side team ships new components
• Identify and resolve latency, cost, and reliability issues before they become production incidents
• Work closely with the client's MCP technical lead — small team, no bureaucracy, your infrastructure decisions are immediately visible

AI/ML Engineer – MCP Infrastructure

• Own the client's recommendation system end-to-end: design, training, deployment, and monitoring
• Build and mature ranking and retrieval models that process behavioral signals — clicks, opens, purchases — at consumer scale
• Operate ML systems at hundreds of millions of events per day, with direct impact on client revenue
• Make independent technical decisions and engage directly with client engineering leadership — no intermediaries

Senior ML Engineer

• Configure and tune the AI voice agent on the Retell platform — conversation flows for call greeting, intent detection, caller routing, wire verification, closing date inquiries, and escalation handling
• Write and iterate on LLM prompts that control the agent's personality, decision-making, and compliance behavior
• Design multi-step conversation workflows with branching logic (e.g. caller asks about wire instructions → verify identity → look up file → confirm or escalate)
• Configure the agent's knowledge base — staff directories, office information, FAQs, and process explanations
• Work on the telephony integration layer — understanding how calls route from the PBX through to the agent and how the agent transfers calls back to specific extensions
• Own the analytics and quality layer — call scoring, sentiment analysis, intent categorization, and accuracy monitoring using Retell's analytics and custom tooling
• Collaborate closely with the backend engineer who builds the APIs the agent calls, and with the technical lead

AI Engineer – Conversational AI, Voice, LLM

• Design, build, and maintain production recommendation systems at consumer scale.
• Develop ranking and retrieval models to improve personalization and user experience.
• Own ML pipelines end-to-end, from feature engineering and training to deployment and monitoring.
• Collaborate with client stakeholders and engineering teams to deliver scalable ML solutions.
• Analyze large-scale user behavior data and run A/B tests to optimize recommendation performance.
• Improve reliability, scalability, and latency of production ML systems on AWS/GCP/Azure.

Machine Learning Engineer – Recommendation Systems

• Architect AI Solutions: Build and deploy production-grade AI features, including RAG (Retrieval-Augmented Generation) pipelines, LLM orchestrations, and Agentic workflows using Vertex AI.
• Develop Core Logic: Write clean, maintainable, and highly efficient code in Python to support AI model integration and data-intensive applications.
• Optimize Model Performance: Fine-tune models for specific business logic and optimize LLM prompts to ensure accuracy, relevance, and safety for enterprise use cases.
• Engineer Data Sources: Optimize and manage data sources and vector databases to ensure high-quality retrieval for context-aware AI systems.
• Collaborate Globally: Work closely with US-based product owners and European delivery teams, requiring a proactive communication style and flexibility for early-morning syncs.
• Ensure Quality: Champion best practices in AI observability, evaluation frameworks, and automated testing to ensure the reliability of enterprise-level deployments.
• Consult with Clients: Partner with clients to develop new AI concepts and enhancements, translating business goals into technical AI roadmaps.

Senior AI Engineer

• Lead the evolution of LLM and agent-based architectures
• Design systems for high concurrency (voice, chat, event-driven)
• Improve orchestration, observability, and control layers
• Solve real production challenges: latency, stability, cost
• Drive an AI-native engineering culture

AI Engineering Lead

• Build and deliver AI-powered features based on functional and technical requirements provided by TPMs and Principal AI Engineers
• Develop end-to-end AI solutions, including backend services, APIs, and application-layer integrations
• Integrate large language models (LLMs) into production systems, ensuring performance, scalability, and reliability
• Design, test, and optimize prompts to improve output quality and consistency
• Collaborate with ML Engineers to embed models trained or fine-tuned on proprietary data into applications
• Participate in model evaluation and validation, ensuring outputs meet quality, accuracy, and performance benchmarks
• Support testing and deployment of AI features through CI/CD pipelines
• Troubleshoot and refine AI behaviours in real-world production scenarios.

• Design, build, and deploy end-to-end machine learning models using proprietary datasets
• Own the full ML lifecycle, including data preparation, feature engineering, model training, evaluation, and validation
• Collaborate with Data Scientists to identify and prioritize high-impact ML opportunities
• Work with Principal AI Engineers to ensure models align with system architecture and engineering best practices
• Package and expose trained models as scalable APIs for consumption by AI Engineers and downstream systems
• Build and maintain model pipelines, including versioning, retraining, and continuous improvement workflows
• Monitor model performance in production, ensuring accuracy, reliability, and relevance over time
• Troubleshoot model drift, data quality issues, and performance bottlenecks

Senior Machine Learning Engineer

• Build AI-powered solutions for client projects, from discovery through production
• Integrate AI models (OpenAI, open-source LLMs, etc.) into production systems and APIs
• Develop and optimize prompts, retrieval systems (RAG), and agent-based workflows
• Implement guardrails, observability, and monitoring for AI systems
• Optimize performance, latency, and cost of AI applications
• Rapidly prototype solutions to validate ideas and demonstrate value to clients
• Document approaches, experiments, and system architecture clearly
• Work cooperatively with Modus project and account managers, designers, engineers, and other team members, as required
• Report progress/updates and/or communicate with other team members, project leads, and our clients, as required
• Other duties related to the position that may arise from time to time and as may be assigned

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