Senior MLOps Engineer (AWS / GenAI)
⚲ Warsaw
28 560 - 32 760 PLN (B2B)
Wymagania
- AWS
- AWS SageMaker
- AWS Lambda
- AWS Glue
- AWS S3
- AWS EC2
- AWS ECS
- Athena
- Terraform
Opis stanowiska
O projekcie:
💻Ework Group - founded in 2000, listed on Nasdaq Stockholm, with around 13,000 independent professionals on assignment - we are the total talent solutions provider who partners with clients, in both the private and public sector, and professionals to create sustainable talent supply chains.
With a focus on IT/OT, R&D, Engineering and Business Development, we deliver sustainable value through a holistic and independent approach to total talent management.
By providing comprehensive talent solutions, combined with vast industry experience and excellence in execution, we form successful collaborations. We bridge clients and partners & professionals throughout the talent supply chain, for the benefit of individuals, organizations and society.
🔹 For our Client from banking industry we are looking for 🔹 Senior MLOps Engineer (AWS/GenAI)🔹
Location: Warsaw
Hybrid work: 3 days per week at the office.
We are looking for an experienced Senior MLOps Engineer to support the implementation of advanced AI solutions within a large-scale program in the GenAI / AI domain.
In this role, you will be responsible for building and operationalizing MLOps / GenAI Ops / Agentic AI pipelines on AWS, enabling production-grade AI solutions for business-critical use cases.
Wymagania:
🛠️ Must-have skills
Strong hands-on experience with AWS cloud (advanced level)
Practical experience with:
- AWS SageMaker- AWS Bedrock (GenAI / LLMs)
Broad knowledge of AWS ecosystem, including:
- Data: S3, Glue, Glue Catalog, Athena- Compute: Lambda, EC2, ECS, Fargate- Orchestration: Step Functions, EventBridge, SNS- Security & networking: IAM, VPC, KMS- Monitoring: CloudWatch, X-Ray, CloudTrail
Experience in building MLOps pipelines or ML platforms
Hands-on experience with Terraform (IaC)
Experience with tools like MLflow
Strong understanding of ML lifecycle and deployment challenges
Codzienne zadania:
- Design and implement MLOps / GenAI Ops frameworks for production AI use cases
- Build and manage end-to-end ML pipelines (training, deployment, monitoring, retraining)
- Work with LLM-based solutions (GenAI) using AWS Bedrock
- Develop and maintain scalable AI/ML infrastructure on AWS
- Orchestrate workflows using services such as Step Functions, EventBridge, and Lambda
- Provision and manage cloud infrastructure using Terraform (Infrastructure as Code)
- Ensure proper monitoring, logging, and observability (CloudWatch, X-Ray, CloudTrail)
- Collaborate with cross-functional teams (Data, AI, Engineering) to deliver AI solutions
- Support implementation of agent-based AI systems (Agentic AI)
💻Ework Group - founded in 2000, listed on Nasdaq Stockholm, with around 13,000 independent professionals on assignment - we are the total talent solutions provider who partners with clients, in both the private and public sector, and professionals to create sustainable talent supply chains.
With a focus on IT/OT, R&D, Engineering and Business Development, we deliver sustainable value through a holistic and independent approach to total talent management.
By providing comprehensive talent solutions, combined with vast industry experience and excellence in execution, we form successful collaborations. We bridge clients and partners & professionals throughout the talent supply chain, for the benefit of individuals, organizations and society.
🔹 For our Client from banking industry we are looking for 🔹 Senior MLOps Engineer (AWS/GenAI)🔹
Location: Warsaw
Hybrid work: 3 days per week at the office.
We are looking for an experienced Senior MLOps Engineer to support the implementation of advanced AI solutions within a large-scale program in the GenAI / AI domain.
In this role, you will be responsible for building and operationalizing MLOps / GenAI Ops / Agentic AI pipelines on AWS, enabling production-grade AI solutions for business-critical use cases.
Wymagania:
🛠️ Must-have skills
Strong hands-on experience with AWS cloud (advanced level)
Practical experience with:
- AWS SageMaker- AWS Bedrock (GenAI / LLMs)
Broad knowledge of AWS ecosystem, including:
- Data: S3, Glue, Glue Catalog, Athena- Compute: Lambda, EC2, ECS, Fargate- Orchestration: Step Functions, EventBridge, SNS- Security & networking: IAM, VPC, KMS- Monitoring: CloudWatch, X-Ray, CloudTrail
Experience in building MLOps pipelines or ML platforms
Hands-on experience with Terraform (IaC)
Experience with tools like MLflow
Strong understanding of ML lifecycle and deployment challenges
Codzienne zadania:
- Design and implement MLOps / GenAI Ops frameworks for production AI use cases
- Build and manage end-to-end ML pipelines (training, deployment, monitoring, retraining)
- Work with LLM-based solutions (GenAI) using AWS Bedrock
- Develop and maintain scalable AI/ML infrastructure on AWS
- Orchestrate workflows using services such as Step Functions, EventBridge, and Lambda
- Provision and manage cloud infrastructure using Terraform (Infrastructure as Code)
- Ensure proper monitoring, logging, and observability (CloudWatch, X-Ray, CloudTrail)
- Collaborate with cross-functional teams (Data, AI, Engineering) to deliver AI solutions
- Support implementation of agent-based AI systems (Agentic AI)
🔍 Dekoder Ogłoszenia
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Senior MLOps Engineer (AWS / GenAI)
Stanowisko może wymagać zarówno głębokiej wiedzy z zakresu MLOps, jak i praktycznego doświadczenia z GenAI, co może być trudne do znalezienia w jednej osobie.
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advanced AI solutions within a large-scale program in the GenAI / AI domain
Może oznaczać pracę nad bardzo złożonymi i potencjalnie niedojrzałymi technologiami, które wymagają dużo eksperymentowania.
🔴
building and operationalizing MLOps / GenAI Ops / Agentic AI pipelines on AWS
Wymaga to nie tylko budowania, ale i utrzymania w produkcji, co może oznaczać dużą odpowiedzialność i potencjalne problemy.
🔴
business-critical use cases
Oznacza, że Twoja praca będzie miała bezpośredni wpływ na kluczowe procesy biznesowe, co wiąże się z wysoką presją i oczekiwaniami.
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Hybrid work: 3 days per week at the office.
Choć hybrydowy, wymaga regularnej obecności w biurze, co może być mniej elastyczne niż w pełni zdalna praca.