What You’ll Do
As a Machine Learning Engineer at InteractiveAI, you’ll design, train, and productionize models that power our agentic platform. Embedded in a cross-functional squad, you’ll build resilient data and model pipelines, evaluate model quality with rigorous offline/online methods, and ship performant inference services at scale. You’ll collaborate closely with product and delivery to turn business problems into measurable ML solutions.
- Build and maintain scalable pipelines for structured/unstructured data ingestion, transformation, and feature engineering
- Train, evaluate, and iterate on ML models (including LLM fine-tuning where relevant) with strong experiment tracking and reproducibility
- Deploy ML models and LLMs into production, ensuring performance, reliability, observability, and traceability
- Implement automated evaluation (A/B tests, LLM-as-judge, validation suites) and dashboards to monitor latency, accuracy, drift, and trigger retraining or alerts
- Apply feature engineering, imputation, and transformation techniques in practical, production scenarios
- Contribute to retrieval-augmented generation (RAG) workflows and measure retrieval and generation quality
- Integrate enterprise-grade agentic workflows and perform systematic evaluation of LLM outputs
- Optimize inference speed and memory usage in high-throughput systems; profile and reduce cost without sacrificing quality
- Monitor and improve model performance in production (latency, accuracy, drift, data quality) with feedback loops
- 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 ML systems. Here’s what success looks like for this role:
1/ 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 (e.g., 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
2/ 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.
