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

OpsBrasil Serviços Cloud LTDA

WorldwideremotePosted today

Skill Required

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Key highlights

Role: AI/ML Solution Architect
Required Cloud Platform: AWS
Experience Level: Multiple years with recent hands-on AI experience
Education: Bachelor's degree in Computer Science, IT, or related field
Key Technologies: Python, GenAI Ecosystem, SQL, Terraform / CloudFormation
Bonus Qualification: AWS Professional/Specialty Certification

Role overview

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. 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.

Responsibilities

Responsible for technical coordinating a team of 2-3 engineers focused on designing and implementing AI solutions.
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
Designing, building and maintaining CI/CD pipelines in multiple integration and test environments
Staying up-to-date with the AWS innovations and achieving Professional/Specialty level certifications based on personal interest
Contributing to AWS Competency and Service Delivery Program to support AWS Partner journey

Requirements

A bachelor's degree in Computer Science, Information Technology, or a related field
Multiple years of experience, including recent hands-on work on AI projects. Software engineering, data engineering and data science backgrounds are all a strong starting point.
Experience with some of the following technologies: SageMaker, Bedrock, Glue, MWAA, EMR, RDS, Aurora, S3, OpenSearch, ElasticSearch.
Strong proficiency in Python and hands-on experience with the GenAI ecosystem (e.g. Amazon Bedrock AgentCore, LangChain, LlamaIndex, agent frameworks, LLM provider SDKs, Pydantic).
Good command of SQL and a working understanding of core data engineering concepts (ETL, pipelines, data modelling).
Proven experience designing, consuming and integrating APIs, and connecting AI services to existing systems.
Solid understanding of LLM application concepts: RAG, embeddings, vector databases, tool calling, agent patterns, prompt engineering and evaluation.
Working knowledge of AWS, including hands-on use of at least one of its AI/ML or serverless services. Strong cloud-agnostic experience across multiple providers is a plus, but some practical AWS exposure is expected.
Excellent problem-solving and communication skills.
Excellent problem-solving and communication skills, and the ability to work independently and collaboratively in a fast-paced environment.