Senior AI Infrastructure Engineer

Solvd is an AI-first advisory and digital engineering firm delivering measurable business impact through strategic digital transformation. Taking an AI-first approach, we bridge the critical gap between experimentation and real ROI, weaving artificial intelligence into everything we do and helping clients at all stages accelerate AI integration into each process layer. Our mission is to empower passionate people to thrive in the era of AI while maintaining rigorous ethical AI standards. We're supported by a global team with offices in the USA, Poland, Ukraine, Georgia and LATAM
We're looking for a Senior AI Infrastructure Engineer to help design, build, and scale our AI and data infrastructure. In this role, you'll focus on architecting and maintaining cloud-based MLOps pipelines to enable scalable, reliable, and production-grade AI/ML workflows, working closely with AI engineers, data engineers, and platform teams. Your expertise in building and operating modern cloud-native infrastructure will help enable world-class AI capabilities across the organization
If you are passionate about building robust AI infrastructure, enabling rapid experimentation, and supporting production-scale AI workloads, we'd love to talk to you

Responsibilities Design, implement, and maintain cloud-native infrastructure to support AI and data workloads, with a focus on AI and data platforms such as Databricks and AWS Bedrock. Build and manage scalable data pipelines to ingest, transform, and serve data for ML and analytics. Develop infrastructure-as-code using tools like Cloudformation, AWS CDK to ensure repeatable and secure deployments. Collaborate with AI engineers, data engineers, and platform teams to improve the performance, reliability, and cost-efficiency of AI models in production. Drive best practices for observability, including monitoring, alerting, and logging for AI platforms. Contribute to the design and evolution of our AI platform to support new ML frameworks, workflows, and data types. Stay current with new tools and technologies to recommend improvements to architecture and operations. Integrate AI models and large language models (LLMs) into production systems to enable use cases using architectures like retrieval-augmented generation (RAG). Mandatory Requirements 7 years of professional experience in software engineering and infrastructure engineering. Extensive experience building and maintaining AI/ML infrastructure in production, including model, deployment, and lifecycle management. Strong knowledge of AWS and infrastructure-as-code frameworks, ideally with CDK. Expert-level coding skills in TypeScript and Python building robust APIs and backend services. Production-level experience with Databricks MLFlow, including model registration, versioning, asset bundles, and model serving workflows. Expert level understanding of containerization (Docker), and hands on experience with CI/CD pipelines, orchestration tools (e.g., ECS) is a plus. Proven ability to design reliable, secure, and scalable infrastructure for both real-time and batch ML workloads. Ability to articulate ideas clearly, present findings persuasively, and build rapport with clients and team members. Strong collaboration skills and the ability to partner effectively with cross-functional teams. Familiarity with emerging LLM frameworks such as DSPy for advanced prompt orchestration and programmatic LLM pipelines. Understanding of LLM cost monitoring, latency optimization, and usage analytics in production environments. Knowledge of vector databases / embeddings stores (e.g., OpenSearch) to support semantic search and RAG.

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