{"id":2499079,"url":"https://alion.io/job/automat-it-senior-ai-engineer-aws-bedrock","title":"Senior AI Engineer (AWS Bedrock)","company":{"id":2914610,"name":"Automat-it","domain":"automat-it.com","url":"https://alion.io/company/automat-it","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Comeet","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":["UA"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":28000,"max_usd":74000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2365},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon CloudWatch","optional":false},{"name":"Amazon S3","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"AWS Bedrock AgentCore","optional":false},{"name":"AWS CDK","optional":false},{"name":"AWS Lambda","optional":false},{"name":"AWS Strands Agents","optional":false},{"name":"CI/CD","optional":false},{"name":"CloudFormation","optional":false},{"name":"Docker","optional":false},{"name":"DynamoDB","optional":false},{"name":"Fine-tuning","optional":false},{"name":"FinOps","optional":false},{"name":"IAM","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"OpenSearch","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Terraform","optional":false},{"name":"Text-to-Speech","optional":false},{"name":"PostgreSQL","optional":true}],"status":"live","first_seen_at":"2026-10-13T21:58:28Z","employer_posted_date":"2026-10-21","last_verified_at":"2026-11-06T18:19:42Z","board_verified":true,"closed_at":null,"days_open":23,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":23},"description":"Description\nAutomat-it is an all-in AWS Premier Partner and Managed Services Provider specializing in the startup ecosystem. With over 800 customers and 500+ AWS certifications, Automat-it brings hands-on expertise in AI, DevOps, and FinOps to empower fast-paced startups to grow, deliver & win. Our customers save significant time-to-market and optimize their cloud performance and costs.\nWe work across EMEA and the US, fueling innovation and solving complex challenges daily. Join us to grow your skills, shape bold ideas, and help build the future of tech.\nWe’re looking for an AI Engineer (Senior level or strong Middle) to join our team and work directly with startup's Data Science and R&D teams. This is a hands-on, delivery-focused role where you will own projects end-to-end, from early design to production deployment.\nThe role is focused on building production-grade Generative AI systems on AWS, especially RAG pipelines, agent-based workflows, and LLM-powered backend services. This is not a pure research or model training role, it’s about designing and shipping reliable systems under real constraints.\nWork location: remote from Ukraine.\nCurious about what it's really like to work at Automat-it?\nExplore our benefits, culture, and what success in your first year could look like here.\nIf you are interested in this opportunity, please submit your CV in English.\nKey Responsibilities\nBuild and deliver production-ready GenAI systems on AWS, including Amazon Bedrock, AgentCore, RAG systems, intelligent document processing, voice AI, and LLM-powered services.\nDesign and implement AI agents using Amazon Bedrock AgentCore, AWS Strands, MCP, and modern orchestration frameworks for real customer solutions.\nWork closely with Solution Architects, DevOps, and customer teams to turn discovery workshops, ideas, and POCs into production-ready AI systems.\nEvaluate and select the most appropriate LLMs based on accuracy, latency, cost, and customer requirements.\nBuild reusable AI components and deployment patterns that accelerate future customer projects.\nDeploy, monitor, and improve ML/LLM systems in production, focusing on performance, cost, and reliability\nWork with AWS services such as Bedrock, OpenSearch, Lambda, S3, DynamoDB, SageMaker, and CloudWatch\nAdapt existing ML or GenAI code into production environments when needed\nContinuously improve system quality, including retrieval performance, output consistency, and evaluation approaches\nOperate in a fast-paced, project-based environment where you may own a project as the main engineer\nRequirements\nStrong hands-on experience building and deploying AI / GenAI systems in production\nStrong hands-on AWS experience beyond model invocation, including infrastructure, IAM, serverless services, networking, storage, monitoring, and production deployments using Amazon Bedrock.\nExperience building RAG systems in practice, including retrieval logic, vector databases, and output quality improvements\nStrong understanding of modern LLM ecosystems, including commercial and open-source models, their trade-offs, deployment options, and production use cases.\nStrong Python skills and a good understanding of backend system design\nExperience designing multi-agent systems or more complex orchestration workflows\nExperience with vector databases (OpenSearch, pgVector, Pinecone, etc.)\nComfort working in fast-moving environments with short project cycles (weeks to a few months)\nStrong communication skills and ability to work directly with clients and cross-functional teams\nAbility to clearly explain technical decisions, limitations, and trade-offs in English (written and spoken)\nHands-on experience with Amazon Bedrock Knowledge Bases, AgentCore, Agents, AWS Strands, or MCP is a strong advantage.\nExperience selecting, evaluating and optimizing LLMs for quality, latency and cost.\nAbility to explain technical trade-offs and guide customers through AI solution design is a strong advantage.\nExperience with Infrastructure as Code (Terraform, CloudFormation or AWS CDK), Docker, Kubernetes and CI/CD pipelines is a strong advantage.\nExperience with speech-to-text, text-to-speech or Voice AI is an advantage.\nBackground in Machine Learning or Data Science (including model training or fine-tuning) - an advantage\nAutomat-it is committed to fostering a workplace that promotes equal opportunities for all. We firmly believe that cultivating a diverse workforce is crucial to our success. 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