Candidates must be willing to participate in at least one in-person interview, which may include a live whiteboarding or technical assessment session. Bridge the gap between experimental machine learning and production-grade software by engineering systems that orchestrate Large Language Models (LL - Ms) and "agentic" workflows. Build and champion a "Self-Healing" infrastructure that moves operations beyond traditional monitoring into a new era of observability and predictive intelligence. Solve complex business problems by integrating RAG pipelines and autonomous remediation tools to ensure high-performance, intelligent system reliability across cloud environments. What Success Looks Like (Objectives)Implement complex AI workflows using frameworks like Lang. Chain and Llama. Index to drive agentic solutions for enterprise-level challenges. Deliver high-quality RAG pipelines utilizing Milvus vector databases to improve data retrieval accuracy and reduce model hallucinations. Automate Tier-1 and Tier-2 incident resolutions by developing "Runbooks-as-Code" using Python and Ansible for self-healing infrastructure. Establish rigorous evaluation frameworks to measure model performance and accuracy, ensuring production-grade reliability for LLM responses. Champion the transition to AI-native development by mentoring junior engineers and defining engineering best practices across the department. Optimize system performance and cost by engineering microservices that handle asynchronous AI processing and intelligent token management. Skills, Experience and Requirements. Core Skills and Competencies (What you’ll bring)Mastery of backend engineering and API design (RESTful/ Graph. QL) to seamlessly expose AI capabilities to complex frontend applications. Expert proficiency in AI integration and the current LLM landscape, including prompt engineering and the orchestration of Transformer-based models. Advanced technical literacy in AI-specific monitoring and observability tools like Dynatrace, Lang. Smith, or Weights & Biases to track model performance. Critical experience in designing ETL pipelines for unstructured data and managing vector databases to support scalable RAG systems. Strong collaborative leadership and decision-making skills to guide cross-functional teams through the complexities of cloud-native AI development. A fearless and curious approach to problem-solving that enables the rapid adoption of emerging technologies like autonomous agents and predictive intelligence. Additional Qualifications. Contributions to open-source AI projects or a portfolio of "Agentic" AI applications. Certifications in Cloud AI (e.g., AWS Certified Machine Learning – Specialty). Minimum Requirements. Minimum Education: Bachelor’s Degree in Computer Science, Information Technology, or a relevant field. Minimum Experience: 5 years of professional experience in backend or full-stack software development with a focus on AI/ ML integration. Required Technical Skills: - Python or Java for high-traffic application development. Modern AWS services including Serverless, Kubernetes, and EC 2. Automation and configuration tools such as Ansible and Bash scripting. Visa sponsorship not available for this role Salary Ranges. Compensation: $96,250.00/ Year - $137,500.00/ Year Benefits. We offer versatile health perks, including flexible spending accounts, HSA, a 401(k) Plan with company match, ESPP, career opportunities, and a flexible time away plan; all benefits can be viewed 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 based on work location. Candidates need to successfully complete a pre-employment screen, which may include a drug test and DMV check. Our company is committed to fostering an inclusive and equitable workplace where every individual has the opportunity to succeed. We are dedicated to providing individuals with criminal or arrest records a fair chance of employment in accordance with local, state, and federal laws.