EPAM - Senior AIOps Solution Architect

Description :

- Architect and implement Gen-AI & LLM Engineering solutions using tools such as Amazon Bedrock, Azure OpenAI, Vertex AI, Anthropic, and LangChain.

- Develop and optimize MLOps pipelines and model deployment workflows leveraging SageMaker, Azure ML, clustering, topic modeling, and anomaly detection techniques.

- Implement RAG, Vector DBs, and advanced semantic search across platforms using PGVector, Elasticsearch, and Bedrock Knowledge Sources.

- Create and automate solutions for Cloud Platforms and Infrastructure with AWS, Azure, GCP, Terraform, CloudFormation, and Helm, alongside Python and Shell Scripting.

- Lead Kubernetes-based container orchestration and DevSecOps initiatives, including CI/CD pipelines, Istio, and KEDA deployment strategies.

- Design and integrate serverless and cloud-native architectures using API Gateway, Lambda, Step Functions, DynamoDB, S3, and Kinesis.

- Implement end-to-end Observability solutions using DataDog, OpenTelemetry, Dynatrace, New Relic, Splunk, Moogsoft, and BigPanda.

- Ensure seamless ITSM and ServiceNow integration for AI-driven operations and automation.

- Work with ITSM tools like ServiceNow, Jira Service Management, and Manage Engine to streamline incident management workflows.

- Provide thought leadership in AIOps, automation, and AI-powered operational intelligence to leadership and engineering teams.

Requirements :

- 10+ years of professional experience in Enterprise Cloud, Infrastructure Engineering, SRE, Automation, and Architecture roles.

- Proven track record of delivering Gen-AI-powered AIOps solutions in production environments, driving efficiencies like MTTR improvement and operational automation.

- Expertise in Gen-AI and LLM Engineering tools such as Amazon Bedrock, Azure OpenAI, Vertex AI, Anthropic, LangChain, and Bedrock Agents.

- Proficiency in RAG, Vector Databases, and semantic search solutions like PGVector, Elasticsearch, and Bedrock Knowledge Sources.

- Background in MLOps, model development, and machine learning techniques using SageMaker, Azure ML, clustering, topic modeling, and anomaly detection.

- Skills in cloud engineering and automation technologies, including AWS, Azure, GCP, Terraform, CloudFormation, Helm, Python, and Shell Scripting.

- Capability to design and operate Kubernetes-based infrastructure, CI/CD pipelines, security automation, Istio, and KEDA.

- Familiarity with serverless computing and cloud-native tools like API Gateway, Lambda, Step Functions, DynamoDB, S3, and Kinesis.

- Knowledge of Observability platforms such as DataDog, OpenTelemetry, Dynatrace, New Relic, Splunk, Moogsoft, and BigPanda.

- Understanding of ITSM platforms, including ServiceNow, Jira Service Management, and Manage Engine.

- Showcase of AI and Machine Learning expertise in areas like anomaly detection, GenAI implementation, and agentic AI solutions.

- Ability to communicate effectively in both written and spoken English (B2 level or higher).

Nice to have :

- Experience leading AIOps/Cloud Practices or platform engineering organizations.

- Certifications in AWS ML, Cloud Architecture, or AI Leadership.