ML/LLM Technical Architect

ML/LLM Technical Architect

SoftServe

Poland

IF YOU ARE

• Educated with a degree in Computer Science or a related field;
• Experienced in a similar position, including 2+ years of design and implementation of enterprise-scale AI/ML solutions in the AWS cloud using services such as Amazon SageMaker, AWS Bedrock, Amazon Kendra, AWS Glue, AWS Data Pipelines, Amazon Kinesis, Amazon Athena, and Amazon Redshift;
• Designing sustainable, scalable, and secure architectures, performing trade-off analysis of different architecture tactics and patterns, and applying proven architecture design approaches and methodologies, with a focus on GenAI and LLM-based solutions;
• Driving project roll-outs from requirements gathering to go-live and continuous improvement;
• Skilled in designing, building, or operationalizing Agentic AI systems, with hands-on experience leveraging frameworks like Amazon -Bedrock Agents to create autonomous and goal-oriented GenAI solutions;
• Confident in customer-facing tasks related to discovery, assessment, execution, and operations for complex AI/ML/GenAI projects;
• Knowledgeable of EKS/ECS platforms and their design patterns for ML workloads;
• Confident upper-intermediate English speaker;
• Responsible for AI principles and practices, including model explainability, fairness, and security for GenAI (nice to have);
• Experienced in pre-sales, enterprise consulting, or AI/ML advisory roles (would be a plus).

AND YOU WANT TO

• Engage in a wide scope of AI and ML-related work, from foundational machine learning projects to advanced Generative AI applications;
• Bring your deep expertise in cloud architecture, DevOps, and MLOps/LLMOps to analyze and recommend enterprise-grade solutions for operationalizing the full spectrum of AI;
• Develop end-to-end MLOps/LLMOps pipelines on AWS, leveraging an in-depth understanding of the platform, the entire AI lifecycle, and business problems to ensure solutions are delivered efficiently, predictably, and sustainably;
• Prototype and demonstrate innovative solutions, including proof-of-concepts for diverse AI/ML use cases and specific GenAI applications, for clients in customer environments;
• Develop assets, accelerators, and reusable components for your practice, covering both established AI/ML methodologies and emerging GenAI and LLMOps space;
• Communicate use cases, technical requirements, architectural decisions, and expectations clearly with stakeholders at all levels;
• Guide Engineering and Data Science teams on the production lifecycle of ML systems, including best practices for building, deploying, and monitoring traditional ML models and LLM-powered applications;
• Educate Product teams on best practices for putting ML systems, including innovative GenAI features, into production reliably and responsibly.

Apply Now