• Collaborate with the Head of AI, product managers, and engineers • Engineer and deploy ML models into real-time systems • Build robust, low-latency ML pipelines using Python, C++, and SQL • Apply adversarial learning and behavior modeling • Design and implement workflows that enhance data quality • Monitor, evaluate, and optimize deployed models • Present results and trade-offs to stakeholders • Contribute to Blackwall’s AI engineering practices by mentoring peers

• Design and build the core architecture for an enterprise workflow automation platform • Make critical technical decisions about stack, architecture patterns, and implementation independently • Create robust APIs, integrations, and workflow engines adaptable to diverse enterprise needs • Deploy virtually or on-site into customer organizations across the US • Conduct discovery sessions to understand workflows and requirements • Rapidly prototype and build custom solutions tailored to each enterprise • Iteratively integrate AI capabilities to enhance automation and drive value • Act as the technical face of the company during customer engagements • Work autonomously with minimal oversight from CEO and leadership • Make architectural decisions and push back constructively when needed • Move fast, ship quickly, and iterate.

• Design and implement AI/LLM‑powered capabilities including agent workflows, tool‑use actions, retrieval‑based systems, and structured output pipelines. • Build integrations with major model providers (OpenAI, Azure OpenAI, Anthropic) and open‑source model ecosystems. • Develop and optimize RAG pipelines, embeddings, vector search, and semantic retrieval patterns. • Implement evaluation harnesses, guardrails, prompt management, and safety validation workflows. • Collaborate with backend, frontend, and data engineers to deliver scalable AI‑driven features. • Integrate AI capabilities into Kubernetes‑based microservices environments using modern APIs and deployment patterns. • Configure and operate model‑serving environments (vLLM, TGI, KServe) including tuning for latency, throughput, and cost. • Implement observability for AI systems including telemetry, metrics, traces, structured logs, and prompt evaluations. • Support CI/CD automation, model versioning, feature flagging, and safe rollout of AI functionality. • Contribute to documentation, architectural diagrams, and reusable internal AI patterns. • Mentor junior engineers and support skill development across AI engineering best practices.

• Lead the design and implementation of scalable AI architectures that align with client business objectives and industry best practices • Collaborate with account leadership to identify and shape AI opportunities, contributing to business development efforts including RFP responses, white papers, and client presentations • Architect and build AI platforms and pipelines leveraging cloud-native services (AWS, Azure, GCP, Palantir, Databricks) to support model development, deployment, and monitoring • Apply deep knowledge of AI/ML lifecycle, including data preparation, model training, evaluation, and operationalization, with a focus on performance, scalability, and maintainability • Drive hands-on development of AI solutions, including Generative AI and Agentic AI systems, ensuring robust integration with enterprise systems and data sources • Collaborate with cross-functional teams (data engineering, software development, DevOps) to ensure seamless delivery of AI solutions • Provide technical leadership and mentorship to junior AI engineers and data scientists • Ensure AI solutions comply with commercial and public sector standards for responsible AI, data governance, and security.