Lead AI Engineer

What You’ll Do

As a
Lead AI Engineer at InteractiveAI, you’ll operate as the Chief of AI’s right hand, driving the technical direction of our AI stack, accelerating new use cases, and guiding the development of advanced GenAI capabilities across the platform.

You’ll take ownership of the design, development, and deployment of cutting‑edge models, agentic architectures, and fine‑tuning workflows. You will lead experimentation efforts, influence architecture decisions, mentor engineers, and ensure our AI systems are scalable, reliable, and aligned with enterprise requirements.

You’ll work in a cross‑functional squad while also contributing to org‑wide AI standards, frameworks, and best practices.

• Build and maintain scalable pipelines for structured/unstructured data ingestion, transformation, and feature engineering
• Lead the deployment of ML models and LLMs into production, ensuring performance, reliability, and traceability
• Architect and oversee fine‑tuning pipelines for LLMs with versioned checkpoints, evaluation suites, and experiment tracking
• Design and implement automated evaluation frameworks (A/B testing, LLM-as-judge, validation suites) and monitoring dashboards to track latency, accuracy, drift, and trigger retraining or alerts
• Guide feature engineering, imputation, and transformation strategies in complex, real‑world scenarios
• Implement and optimize retrieval‑augmented generation (RAG) workflows, vector search approaches, and knowledge‑grounding strategies
• Lead the development of enterprise‑grade agentic workflows, tooling integrations, and agent evaluation methods
• Optimize inference speed, memory usage, and cost for high‑throughput systems across the platform
• Own reliability and performance of models in production, solving challenges around latency, accuracy, drift, and scaling
• Collaborate with product and delivery teams to ship client‑ready, measurable outcomes and accelerate new AI‑driven features

We’re looking for a
top-tier AI engineer with strong foundations, proven delivery, and the leadership abilities required to build production‑ready, enterprise‑grade AI systems. You should be equally capable of executing hands‑on and guiding the strategic evolution of our platform.

• 5+ years in data engineering, ML engineering, applied AI, or similar deep technical roles
• Experience deploying ML models and LLMs to production at scale, with strong inference optimization skills
• Hands‑on experience with agent orchestration tools (Lang Graph, Llama Index, or similar)
• Experience training deep‑learning models and fine‑tuning LLMs using modern frameworks
• Fluent in Python and experienced with at least one major deep learning library (PyTorch, Tensor Flow, JAX, etc.)
• Strong experience building production‑grade data pipelines (batch or streaming) using tools like Airflow, Spark, Dagster
• Solid understanding of ML theory (bias‑variance tradeoff, probability, metrics, optimization, evaluation, etc.)
• Comfortable with cloud platforms (AWS, GCP, Azure) and containerized deployments
• Excellent communication skills with proven ability to mentor engineers or lead technical work streams

• Experience with LLMs and RAG pipelines in production environments
• Familiarity with vector databases, embeddings, and document retrieval strategies
• Exposure to MLOps practices: model monitoring, reproducibility, CI/CD for ML, automated evaluations
• Experience optimizing inference latency, throughput, and cost at scale
• Experience working in regulated or enterprise environments (e.g., banking, insurance)
• Bonus: prior experience in technical leadership roles, architecture ownership, or acting as a technical right‑hand to a CTO/Chief of AI

• Competitive base salary (from…