Senior AI Engineer

Work Mode: Hybrid (3 days/week in office) Pay Range: $100K/Yr - $120K/Yr Base + Annual Bonus

We are seeking a hands-on Senior AI Engineer with a strong foundation in traditional Machine Learning and practical, real-world experience building and deploying LLM- and GenAI-driven systems. This role focuses on designing, engineering, and hardening production-grade AI solutions that are embedded into business workflows—not research prototypes. You will work in small, high-impact delivery teams (2–3 engineers per initiative) and spend the majority of your time (~70–75%) building systems end to end, while also contributing to solution design, technical decision-making, and cross-functional collaboration.

: AI Solution Design & Problem Solving • Partner with business and product stakeholders to translate real-world problems into practical AI solutions. • Determine when to apply: • Traditional ML approaches (classification, regression, clustering, recommendation systems) • LLM / GenAI approaches, including agentic workflows • Evaluate and communicate trade-offs across accuracy, cost, latency, scalability, and operational complexity. • Design iterative AI workflows and propose alternative solution approaches where applicable. Hands-on Engineering & Delivery (70–75%) • Build and own end-to-end AI systems, including: • Data ingestion and processing pipelines • Feature engineering and prompt construction • ML and LLM integration and orchestration • API-based AI services for downstream consumption • Deploy and harden production AI systems with: • Error handling and fallback mechanisms • Guardrails, safety controls, and exception handling • Observability (logging, metrics, tracing, dashboards) • Ensure production readiness through: • Performance tuning and latency optimization • Cost management and optimization strategies • Scalability and reliability planning • Implement AI system controls such as: • Input validation and prompt injection mitigation • Configurable policies and kill switches • Transition PoCs into production-grade systems through refactoring, testing, and system hardening. ML & Generative AI Expertise • Apply strong fundamentals in traditional ML, including supervised and unsupervised learning techniques. • Build and deploy GenAI solutions, with experience across at least one or two real-world LLM implementations. • Work with modern LLMs (e.g., OpenAI, Claude, Gemini, Llama or equivalent models). • Design and implement RAG (Retrieval-Augmented Generation) architectures. • Apply prompt engineering, evaluation techniques, and iterative optimization. • Build and evolve tool-based and agentic workflows, including multi-agent systems. • Use agent orchestration frameworks (e.g., LangChain, LangGraph, or equivalent custom systems). Collaboration & Technical Leadership (25–30%) • Act as a senior technical contributor within small delivery teams. • Debug complex AI system behavior and production issues beyond prompt-level tuning. • Contribute to architectural and design decisions alongside architects and platform teams. • Collaborate closely with: • Product managers and business stakeholders • Platform, cloud, and infrastructure teams • Uphold strong software engineering practices and delivery discipline.