What You’ll Do
As an AI Engineer at InteractiveAI, you’ll lead experimentation and deployment of cutting-edge models, agentic architectures, and fine-tuning workflows - shaping the core systems that power our autonomous agents. Embedded in a cross-functional squad, you’ll design and implement advanced AI systems that integrate reasoning, memory, and tool use to solve real-world business problems. You’ll own end-to-end model and data pipelines, support scalable LLM deployments, and contribute to robust, production-grade AI infrastructure.
- Build and maintain scalable pipelines for structured/unstructured data ingestion, transformation, and feature engineering
- Deploy ML models and LLMs into production, ensuring performance, reliability, and traceability
- Build streamlined fine-tuning pipelines for LLMs with versioned checkpoints and hyperparameter tracking
- Implement automated evaluation (A/B tests, LLM-as-judge, validation suites) and dashboards to monitor latency, accuracy, drift, and trigger retraining or alerts
- Feature engineering, imputation, and transformation techniques in practical scenarios
- Implement retrieval-augmented generation (RAG) workflows and evaluate performance
- Implement enterprise-grade agentic workflows and evaluate LLM outputs
- Optimize inference speed and memory usage in high-throughput systems
- Monitor and improve model performance in production, including latency, accuracy, and drift
- Work alongside product and delivery leads to ensure client-ready, measurable outcomes
What We’re Looking For
We’re looking for someone with strong foundations, proven delivery, and the ability to build production-ready AI systems. Here’s what success looks like for this role:
Minimum Requirements:
- 3+ years in data engineering, ML engineering, or applied AI roles
- Experience deploying models to production and optimizing inference performance
- Hands-on experience with at least one agent orchestration tool (LangGraph, LlamaIndex)
- Experience training deep-learning models and fine-tuning LLMs
- Fluent in Python for data and ML development and hands-on experience with at least one deep learning framework (PyTorch, TensorFlow, etc.)
- Experience building data pipelines (batch or streaming) using tools like Airflow, Spark
- Solid grasp of ML concepts (bias-variance tradeoff, supervised vs. unsupervised learning, precision-recall tradeoffs)
- Comfortable working with cloud platforms (AWS, GCP, or Azure)
- Strong communication skills and experience working in cross-functional teams
Additional Requirements:
- Experience with LLMs and RAG pipelines in production
- Familiarity with vector databases, embeddings, and document retrieval strategies
- Exposure to MLOps practices: monitoring, reproducibility, CI/CD for ML
- Experience optimizing inference latency and cost at scale
- Experience working in regulated or enterprise environments (e.g., banking, insurance)
Interview Process
We keep our process focused and respectful of your time. Most candidates complete it in 2–3 weeks. Here’s what to expect:
- Intro Call – 30 minutes with our team to align on fit and expectations
- Take-Home Challenge – A practical task based on real-world problems
- Technical Interview – Deep dive into the challenge, technical experience, and AI engineering
- Cultural and Values Interview – Discussion on motivation, cultural and value alignment
- Offer – Final conversation and offer
We’re building a team of builders — people who care about impact, quality, and growth. If that’s you, let’s talk — careers@interactive.ai
About us
InteractiveAI is a fast-growing startup on a mission to empower enterprises with fully managed AI agent lifecycles.
We are building the next generation of enterprise-AI solutions, delivering an end-to-end Agentic IDE alongside an extensible ecosystem of agentic resources and solutions.
Our platform allows companies to orchestrate, monitor, evaluate, deploy and improve AI agents—and soon fine-tune and own their own models.
We value autonomy, speed, and innovation, and we’re building a world-class team to match. Our squads are lean, focused, and execution-driven.
If you thrive in high-performance environments and want to be part of a company that rewards transformational outcomes, this is for you.
