Role Description We are looking for a Senior / Strong Middle MLOps Engineer to own ML infrastructure, model deployment, and data pipelines across the platform. This is a hands-on role at the intersection of MLOps, DevOps, and Data Engineering, focused on scaling AI systems and bringing models into stable, cost-efficient production. Responsibilities - Build and maintain scalable ML infrastructure for training and inference - Deploy and optimize ML models (batch and real-time) - Work with embeddings pipelines and vector databases - Optimize performance and cost of model deployments - Manage Kubernetes environments (EKS/GKE or similar) - Implement Infrastructure as Code (Terraform) - Build and maintain ETL/ELT pipelines - Optimize database performance (Postgres, large-scale data) - Improve CI/CD pipelines and deployment workflows - Implement monitoring and observability (Prometheus, Grafana) - Collaborate with AI engineers to productionize models Qualifications - 3–5+ years in MLOps / DevOps / Data Engineering - Strong Python skills - Hands-on Kubernetes experience - Experience with AWS or similar cloud - Experience deploying ML models to production - Solid CI/CD and Docker experience - Strong SQL and database experience (PostgreSQL) - Experience with Terraform or other IaC tools Requirements - Experience with large-scale inference or embeddings pipelines - Performance and cost optimization of ML systems - Experience with Airflow, MLFlow, Spark, or DBT - Experience with vector DBs and RAG systems - Exposure to LLM-based systems (LangChain, OpenAI, etc.) Benefits - Experience with AI-first or agent-based platforms - Experience with multi-tenant SaaS architectures - Multi-cloud experience (AWS + GCP) Key Competencies - Strong ownership and hands-on mindset - Ability to work across MLOps, DevOps, and Data domains - Focus on performance, scalability, and cost optimization - Comfortable working in fast-paced startup environment

• Design, implement, and maintain evaluation frameworks to measure model accuracy, robustness, latency, and real-world performance across ASR and NLP systems. • Lead ASR quality improvement efforts, including error analysis, dataset curation, metric definition (e.g., WER and task-specific metrics), and model iteration. • Analyze large-scale speech and text data to identify failure modes and drive targeted model and data improvements. • Develop, train, and deploy machine learning models for speech recognition and downstream tasks such as classification, entity recognition, information extraction, and structured insight generation. • Partner with applied research to translate experimental improvements into production-ready systems. • Collaborate with product managers, platform engineers, and UX teams to align model quality metrics with customer and business goals. • Optimize ML pipelines and evaluation workflows to operate efficiently and reliably at scale. • Establish best practices for model validation, offline/online evaluation, and continuous quality monitoring in production.

• Design and build production-grade ML systems, including LLM-based applications and AI agents • Develop and maintain scalable data pipelines for training and evaluating machine learning models • Work with large multimodal datasets (text, images, video) and build efficient data processing workflows • Improve model quality, evaluation pipelines, and automated metrics • Implement and optimize training and inference pipelines, including GPU optimization where needed • Experiment with modern generative AI approaches, including LLMs and multimodal models • Collaborate closely with engineers and product teams to deliver robust ML solutions in production • Continuously improve system performance, reliability, and scalability

• Design, implement, train, and iterate on novel deep learning models for TCR–pMHC specificity prediction. • Extend and adapt advances in protein language models, structure prediction, generative modeling, and representation learning to the immune receptor setting. • Utilize scalable training infrastructure to support large-scale model development and experimentation. • Conduct rigorous benchmarking and evaluation strategies to ensure models are scientifically sound and practically superior. • Translate biological principles of T cell recognition into principled modeling decisions. • Influence large-scale experimental data generation to maximize modeling leverage and long-term performance gains. • Provide input and technical recommendations to broader modeling discussions and roadmap planning. • Work closely with computational biology, immunology, translational, and engineering teams to ensure models are robust, reproducible, and aligned with overall product goals. • Communicate modeling insights, approaches, and results to cross-functional scientific audiences. • Contribute to publications, presentations, etc. through technical execution and analysis.