AI/ML Solutions Architect ( 107-10SENG-01 )

Are you an experienced AI /ML Solution Architect with experience in AWS? If so, here is your chance to utilize your skills on upcoming international projects with our highly qualified engineering team . You will see a lot of ideas move from POC to MVP to production, which means fast iteration and quick feedback on whether something actually works. What we build has to survive contact with real users, real data and real infrastructure.

As an AI/ML Solutions Architect you will be part of unique AI/ML Service Line with a specific focus on delivering AI and data solutions across various projects. Furthermore, you will be responsible for technical coordinating a team of 2-3 engineers focused on designing and implementing AI solutions. Our current work centres on agentic automation, LLM-based RAG systems, Agentic systems, vector databases, and integrating AI into our clients' existing application landscapes. You will also work closely with colleagues from our application development and cloud migration service lines, on projects where AI, engineering and cloud come together.

What you’ll be doing:

• Technically manage a team of 2-3 AI and cloud engineers focused on fast prototyping of AI/ML solutions in AWS cloud.

• Provide mentoring and technical support to junior and medium teammates.

• Act as a trusted advisor to clients.

• Develop highly scalable services realizing AI/ML functionalities (e.g. LLM, ML and AI models).

• Design and build agentic systems that plan, call tools and automate real business workflows (Amazon Bedrock AgentCore).

• Develop LLM-powered applications, including RAG pipelines: chunking, embedding, retrieval strategies, evaluation and iteration.

• Work with vector databases and search: indexing strategies, hybrid and semantic search, relevance tuning.

• Integrate AI services with external systems through APIs: REST endpoints, authentication, webhooks, third-party and client-side SaaS platforms.

• Perform prompt engineering, model selection and systematic evaluation of LLM outputs.

• Build scalable Python services and the data flows behind them (ingestion, transformation, document preparation), including AI-driven data access such as Text2SQL.

• Use AWS cloud to implement AI solutions (e.g. Bedrock, SageMaker, Lambda, Textract).

• Understanding customers’ business problems and providing technology consulting to identify the best-fit solution

• Owning the end-to-end solution as a key decision-maker and driving the delivery team

• Describing infrastructures using Terraform (Terragrunt) or AWS CloudFormation