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Salary: USD 180,000 - 230,000 / annual
Turing is a leading research accelerator for frontier AI labs and enterprise AI deployment partner. The company works with global enterprises to deploy advanced AI systems and builds proprietary intelligence systems that integrate AI into mission-critical workflows.
As a Forward Deployed AI Engineer, you will be a hands-on engineer embedded within customer projects, responsible for the end-to-end deployment of generative AI applications. You'll partner directly with customers to design, build, and deploy intelligent applications using Python, Langchain/LangGraph, and large language models, bridging engineering excellence with customer empathy to solve high-impact real-world problems.
Key responsibilities include:
- Lead end-to-end deployment of GenAI applications from discovery through delivery
- Architect and implement robust, scalable solutions using Python, Langchain/LangGraph, and LLM frameworks
- Act as a trusted technical advisor, understanding customer needs and crafting tailored AI solutions
- Collaborate with product, ML, and engineering teams to influence roadmap and platform capabilities
- Write clean, maintainable code and build reusable modules for future deployments
- Operate across cloud platforms (AWS, Azure, GCP) to ensure secure, performant infrastructure
- Continuously improve deployment tools, pipelines, and methodologies
Required qualifications include 5–8+ years of software or solutions engineering experience (ideally customer-facing), proven expertise in Python, Langchain, LangGraph, and SQL, deep experience with engineering architecture (APIs, microservices, event-driven systems), demonstrated success designing and deploying GenAI applications to production, strong proficiency with AWS, GCP, and/or Azure, excellent communication skills, and ability to work autonomously managing multiple deployment tracks.
Preferred qualifications include familiarity with CI/CD, infrastructure-as-code (Terraform, Pulumi), container orchestration (Docker, Kubernetes), background in LLM fine-tuning, RAG, or AI/ML operations, and experience in startups, consulting, or fast-paced customer-obsessed environments.
Education requirement: Bachelor's, Master's, or Ph.D. in Computer Science, Engineering, or related technical field.
