Senior AI Engineer

EchoStar is reimagining the future of connectivity. Our business reach spans satellite television service, live-streaming and on-demand programming, smart home installation services, mobile plans and products. Today, our brands include Boost Mobile, DISH TV, Gen Mobile, Hughes and Sling TV.

About the role

Enterprise operations require AI-driven solutions to optimize network performance, elevate customer experience, and streamline workflows. This role addresses the need for scalable ML models, real-time analytics frameworks, and intelligent automation pipelines across enterprise and cloud-native environments. The position bridges strategy and deployment by transforming complex business requirements into robust, secure AI microservices that deliver measurable performance outcomes.

Responsibilities

• Build and deploy autonomous AI agents and securely grounded RAG pipelines using Amazon Bedrock, OpenSearch, and Model Context Protocol (MCP) to execute complex business tasks
• Implement Guardrails for Amazon Bedrock and cloud security protocols to ensure responsible AI usage, data privacy, and prompt injection protection across enterprise applications
• Design and maintain low-latency RESTful APIs and MLOps pipelines on AWS infrastructure to support continuous model training, evaluation, fine-tuning, and real-time inference
• Partner with enterprise engineers, data scientists, and business stakeholders to align AI engineering deliverables with departmental OKRs and operational goals
• Optimize network performance and customer experience by integrating scalable deep learning, NLP, and LLM microservices into existing backend enterprise systems

Requirements

• Bachelor’s Degree in Computer Science, Data Science, AI/ML, or an applicable technical degree
• 4+ years of experience in AI/ML engineering, cloud software development, or a related role
• 4+ years of experience with Python, Pandas, NumPy, and API development using FastAPI
• 4+ years of experience with AWS AI stack including Amazon Bedrock (Knowledge Bases, Guardrails, Agents) and Amazon SageMaker
• 4+ years of experience with Docker, containerization, CI/CD pipelines, and production MLOps workflows

Skills

• Critical experience in building and deploying end-to-end Generative AI applications, RAG frameworks, and agentic workflows in production cloud environments
• Advanced technical proficiency in Python, asynchronous API development using FastAPI, and data manipulation with Pandas and NumPy
• Demonstrated mastery of MLOps best practices, including CI/CD automation, containerization with Docker, model monitoring, and cloud orchestration on AWS
• Strong problem-solving abilities and AI application skills focused on integrating Model Context Protocol (MCP) standards and managing large data processing pipelines
• Collaborative leadership and clear decision-making skills to translate cross-functional business requirements into secure, compliant technical solutions
• Familiarity with Amazon NLX and multi-agent frameworks such as LangGraph or AutoGen

Pay

Compensation: $110,688.00/Year - $137,500.00/Year

Benefits

• Flexible spending accounts and HSA
• 401(k) Plan with company match
• Employee Stock Purchase Plan (ESPP)
• Career opportunities
• Flexible time away plan