Senior Machine Learning Engineer (AI Platform & GenAI)

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Top Skills' Details

1. AI/ML Platform Architecture & AWS Engineering (SageMaker, ECS/EKS, API Gateway, Real-Time Inference, End-to-End System Design)
2. MLOps & Feature Platform Engineering (Feature Stores, Model Serving, CI/CD, MLflow, Monitoring, Drift Detection, Observability)
3. Production GenAI & Agentic AI Systems (Bedrock, Vertex AI, Gemini, RAG, MCP, LangGraph, LLM Application Development)

Secondary Skills - Nice to Haves

• Snowflake
• App Dev

Job Description

***Position Overview***

Client is seeking a Senior Machine Learning Engineer to join a centralized AI Engineering team responsible for designing, building, and supporting enterprise AI, machine learning, and generative AI solutions across the organization. This team enables a wide range of initiatives spanning traditional machine learning, generative AI, agentic AI, and enterprise-scale AI platforms.

This role is focused on engineering and platform development rather than model research. The ideal candidate will have a strong software engineering and cloud architecture background with experience designing and implementing production AI systems from concept through deployment and support. Candidates should be comfortable owning solution architecture, infrastructure design, application integration, model deployment, observability, and ongoing operational support.

The team partners closely with Data Scientists, Product Teams, and Business Stakeholders to transform AI use cases into scalable production solutions while ensuring performance, reliability, security, and maintainability.

Key Responsibilities
- Design and architect end-to-end AI, machine learning, and generative AI solutions within AWS cloud environments.
- Build and deploy real-time and batch inference applications using services such as SageMaker, ECS, API Gateway, and related cloud technologies.
- Develop enterprise-grade generative AI and agentic AI applications leveraging Gemini, Vertex AI, LLMs, RAG architectures, and orchestration frameworks.
- Design and implement scalable feature store solutions supporting both real-time and batch machine learning workflows.
- Build and maintain CI/CD pipelines, infrastructure-as-code deployments, model serving frameworks, and automated operational processes.
- Develop integration patterns connecting AI applications to enterprise systems, APIs, data platforms, and business workflows.
- Implement monitoring, observability, model performance tracking, and SLA management for production AI systems.
- Optimize AI applications for scalability, reliability, latency, cost, and overall production performance.
- Collaborate with Data Scientists to operationalize machine learning models and accelerate production deployment.
- Provide technical leadership on architecture decisions, cloud service selection, and AI engineering best practices.

Required Qualifications
- 5+ years of experience in Machine Learning Engineering, AI Platform Engineering, Software Engineering, or Cloud Engineering.
- Strong hands-on AWS experience including SageMaker, ECS, API Gateway, IAM, CloudWatch, and cloud-native application design.
- Experience designing and deploying production machine learning or generative AI applications.
- Experience building and supporting real-time inference systems and distributed cloud applications.
- Strong Python development experience and software engineering fundamentals.
- Experience building CI/CD pipelines, containerized applications, and infrastructure automation.
- Experience with feature stores, streaming data architectures, and machine learning operationalization.
- Experience supporting production environments through monitoring, observability, troubleshooting, and performance optimization.
- Strong communication skills with the ability to work across engineering, product, and data science teams.
- Preferred Qualifications
- Experience with Vertex AI, Gemini, Agentic AI, RAG architectures, LangChain, LangGraph, or similar AI application frameworks.
- Experience with Snowflake and enterprise data platforms.
- Experience with Kubernetes, Docker, and cloud-native application deployment.
- Experience supporting multiple AI use cases across machine learning, generative AI, and platform engineering environments.
- Prior experience serving as a technical lead, solution architect, or AI platform owner.