Sr AI Engineer

Role :
Sr Staff AI Engineer Location :
Dallas, TX ( Hybrid role - NO REMOTE)
* Lead Architecture and design skills required The Sr Staff AI Engineer is a senior technical leader responsible for designing, building, and operationalizing advanced AI and machine learning solutions that address complex, real-world business challenges, with a strong emphasis on Generative AI, LLMs, AI agents, and scalable AI software systems. This role blends hands-on architecture and coding with customer advisory and cross-functional collaboration to accelerate AI development and deliver measurable operational and financial outcomes.

The ideal candidate brings deep experience building and deploying enterprise AI solutions, not just using AI tools. This person can independently define AI architectures, lead proof-of-concepts through production deployment, and guide teams and customers in adopting AI capabilities that are scalable, reliable, secure, and aligned to business value.

Lead the architecture, design, development, and operationalization of AI-powered applications with a focus on Generative AI, LLMs, RAG systems, AI agents, and intelligent automation. Architect end-to-end AI/ML solutions and translate them into clear architecture diagrams and technical documentation, enabling engineering teams to implement scalable, production-ready systems. Design and implement production-grade AI solutions, including prompt and context engineering, retrieval-augmented generation, multi-agent orchestration, and model evaluation.

Build and optimize end-to-end AI pipelines, including document ingestion, chunking, embeddings, vector search, retrieval, reranking, response generation, and observability. Develop scalable AI services and APIs using Python and modern cloud-native technologies. Evaluate and integrate LLMs, embedding models, vector databases, AI frameworks, and cloud AI services based on performance, cost, scalability, and business fit. Architect production-ready AI systems covering orchestration, model serving, prompt management, evaluation, monitoring, security, and responsible AI practices.

Lead rapid experiments, proof-of-concepts, and customer pilots, then evolve successful solutions into scalable production implementations. Translate complex business problems into AI-driven predictions, recommendations, and workflow automations that deliver measurable outcomes such as cost reduction, service improvement, and revenue uplift. Partner directly with customers, product managers, architects, engineering teams, UX, Dev Ops, and advisory teams to identify high-value AI use cases and ensure successful delivery.

Integrate AI capabilities into enterprise software platforms, microservices, and existing workflows while balancing performance, cost, explainability, and adoption. Guide customers and internal stakeholders through AI strategy, implementation, adoption, and change management. Establish AI engineering standards, design patterns, architecture guidelines, and delivery best practices across teams. Mentor senior engineers, and influence architecture and engineering decisions across multiple teams. Stay current on advancements in AI, machine learning, LLMs, and domain-specific AI applications, applying new techniques where they create tangible value.

12+ years of hands-on professional experience in software engineering, with at least 4–6 years specifically in AI/ML systems AI engineering, or related enterprise-scale technical roles. Strong hands-on experience designing and building AI/ML or Generative AI applications in production environments. Strong programming skills, with experience building production-grade services, APIs, and scalable distributed systems. Strong working knowledge of SQL and modern data engineering practices, including scalable data pipelines, and enterprise data integration.

Hands-on experience with modern AI and GenAI capabilities, including LLMs, RAG architecture, context and harness engineering, agent-based systems, and multi-agent orchestration. Experience evaluating AI systems for quality, latency, reliability, hallucination risk, security, and cost. Experience with AI/ML frameworks and orchestration tools or similar technologies. Experience with cloud AI platforms such as Azure OpenAI or equivalent services. Experience with vector databases and search technologies such as Azure AI Search, Elasticsearch/Open Search, or similar platforms.

Experience with CI/CD, containers, Kubernetes, monitoring, and production deployment practices. Strong collaboration and communication skills, with the ability to explain AI strategy, technical tradeoffs, risks, and value to technical, business, and executive stakeholders.

The salary range information provided, reflects the anticipated base salary range for this position based on current national data. Minimums and maximums may…