AI Engineer II
$83.16k - $136.62kEchoStar
What Success Looks Like (Objectives):
Candidates must be willing to participate in at least one in-person interview, which may include a live whiteboarding or technical assessment session.
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.
What Success Looks Like (Objectives)
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
Core Skills, Competencies, and Key Experiences (What you’ll bring)
Core Skills and Competencies (What You'll Bring)
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
Additional Qualifications
Familiarity with Amazon NLX and multi-agent frameworks such as LangGraph or AutoGen
Minimum Requirements
- Minimum Education: Bachelor’s Degree in Computer Science, Data Science, AI/ML, or an applicable technical degree
- Minimum Experience: 2+ years of experience in AI/ML engineering, cloud software development, or a related role
- Required Technical Skills: Must have at least 2 years of experience with:
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Python, Pandas, NumPy, and API development using FastAPI
AWS AI stack including Amazon Bedrock (Knowledge Bases, Guardrails, Agents) and Amazon SageMaker
Docker, containerization, CI/CD pipelines, and production MLOps workflows
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Salary Range Compensation: $83,160.00/Year - $136,620.00/Year
About Our Department
Our Technology teams challenge the status quo and reimagine capabilities across industries. Whether through research and development, technology innovation or solution engineering, our team members play a vital role in connecting consumers with the products and platforms of tomorrow.
About Our Company 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.
We offer meaningful health perks, including flexible spending accounts, HSA, a 401(k) plan with company match, employee stock purchase plan, career opportunities, and a flexible time away plan. Review a benefits summary here: EchoStar Benefits
The base pay range shown is a guideline. Individual total compensation will vary based on factors such as qualifications, skill level, and competencies. Compensation is based on the role's location and is subject to change.
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