About the Role
As a Senior AI/ML Engineer at Rensora, you will design, train, and deploy the machine learning models that power intelligent automation across our two core platforms — Evolis AI (agentic lifecycle management) and ModernZAI (enterprise application modernization). You will work at the intersection of applied research and production engineering, turning cutting-edge ML techniques into reliable, scalable systems that enterprise clients depend on daily.
Your work will span the full ML lifecycle: from data pipeline design and model training to inference optimization and production monitoring. You will build and maintain RAG pipelines, integrate large language models into agentic workflows, fine-tune foundation models for domain-specific tasks, and architect the ML infrastructure that makes it all run at scale.
This is a high-impact, high-autonomy role. You will collaborate closely with platform engineers, product managers, and domain experts to identify where machine learning can deliver the most value — and then own the end-to-end delivery of those solutions. If you thrive on solving hard problems at the boundary of research and production, this role is for you.
What You'll Do
- Design and build production ML pipelines for model training, evaluation, and deployment across Evolis AI and ModernZAI
- Develop and optimize RAG (Retrieval-Augmented Generation) pipelines that ground LLM outputs in enterprise knowledge bases
- Fine-tune large language models and foundation models for domain-specific tasks such as code analysis, document understanding, and workflow automation
- Architect and implement inference optimization strategies — including model quantization, caching, batching, and GPU resource management — to meet enterprise latency and cost requirements
- Build and maintain vector database infrastructure for semantic search and retrieval across large-scale enterprise datasets
- Design experiment tracking and model versioning workflows using tools like MLflow to ensure reproducibility and governance
- Collaborate with platform engineers to deploy ML models on Kubernetes-based infrastructure with proper monitoring, alerting, and rollback capabilities
- Evaluate emerging ML techniques and frameworks, and drive adoption of those that deliver measurable improvements to product capabilities
What We're Looking For
- 5+ years of experience in machine learning engineering, with a strong track record of deploying models to production environments
- Deep proficiency in Python and at least one major ML framework (PyTorch or TensorFlow)
- Hands-on experience with large language models — including prompt engineering, fine-tuning, and inference optimization
- Experience building and operating RAG systems with vector databases (Pinecone, Weaviate, Milvus, pgvector, or similar)
- Strong understanding of ML infrastructure: model serving, experiment tracking, data pipelines, and CI/CD for ML
- Experience with cloud platforms (AWS or GCP) and container orchestration (Kubernetes)
- Solid software engineering fundamentals — clean code, testing, version control, and code review practices
- Excellent communication skills and the ability to translate complex ML concepts for non-technical stakeholders
Nice to Have
- Experience with agentic AI frameworks (LangChain, LlamaIndex, AutoGen, or custom agent architectures)
- Background in enterprise software modernization, code analysis, or legacy system migration
- Publications or open-source contributions in NLP, information retrieval, or applied ML
- Experience with MLOps platforms (Kubeflow, SageMaker, Vertex AI) at scale
- Familiarity with distributed training frameworks and multi-GPU/multi-node training setups
Why Rensora
- Remote-first, async culture
- Deep technical work — not ticket factories
- Competitive compensation and equity
- Continuous learning budget
- Work with cutting-edge AI systems at enterprise scale