Turing - AI Engineering Lead, New York
Requirements
• 12+ years of professional experience as a software engineer and building applications/systems. • 2+ years of hands-on experience in how LLMs work & Generative AI (LLM) techniques, particularly multi-agent systems. • Expert proficiency in programming skills in Python, Langgraph, and SQL is a must. • Expert in architecting GenAI applications/systems using various frameworks & cloud services. • Expert proficiency in using AI tools like Claude Code, Codex, cursor, windsurf, and the like. • Expert proficiency in AI observability & evaluation tools like Langsmith, Langfuse, or similar. • Good proficiency in using various cloud services from Azure, GCP, or AWS for building GenAI applications. • Experience in driving the engineering team toward a technical roadmap. • Excellent communication skills to effectively collaborate with business SMEs. • Develop LLM-based solutions: Lead the design, training, fine-tuning, and deployment of large language models, leveraging techniques like retrieval-augmented generation (RAG) and multi-agent-based architectures. • Build and maintain agent evaluation pipelines, including offline eval datasets, LLM-as-judge, and CI-integrated eval runs. • Codebase ownership: Build & maintain high-quality, efficient code in Python (using frameworks like LangChain/LangGraph) and SQL, focusing on reusable components, scalability, and performance best practices. • Cloud integration: Deployment of GenAI applications on cloud platforms (Azure, GCP, or AWS), optimizing resource usage and ensuring robust CI/CD processes. • Communication & Cross-functional collaboration • Communication & • Cross-functional collaboration • Actively follows the frontier and has differentiated, up-to-date views on model releases, agentic architectures, evaluation methods, tool-use and computer-use patterns, multimodal capability, reasoning/test-time compute trends, and the serious open questions in the field. • Produce a structured, high-signal answer to an open-ended technical or strategic question — while modulating depth for a non-engineering executive audience. • Work closely with product owners, data scientists, and business SMEs to define project requirements, translate technical details, and deliver impactful AI products.
Responsibilities
• Client Relationship and Communication: • Lead discussions with clients and prospects to build AI product pipeline and expand to more processes. • Lead discussions with clients • Prioritize client engagement, strengthen relationships, and manage expectations proactively. • Act as a point of contact for client communication, feedback and escalations. • Evaluate and select use-cases for new product development and unlock new revenue opportunities for the client • Evaluate and select use-cases • Participate in client’s internal stakeholder meetings (when applicable) to capture, clarify, and consolidate requirements across functions, ensuring all inputs are properly documented and mapped into actionable product needs. • Tech Leadership: • Provide technical direction to the engineering team on appropriate solutioning & system design for a given problem statement • Align the engineering team toward a technical roadmap and ensure timely execution of the roadmap to achieve customer satisfaction. • Study new LLM research topics and engage in conversation on same with client stakeholders to showcase technical prowess and build value for Turing. • Team Leadership and Coordination: • Drive cross-functional alignment across engineering, product, and client success teams. • Drive cross-functional alignment • Remove blockers for both the client & internal teams by ensuring smooth communication and effective prioritization flows. • Remove blockers • Conduct regular 1:1 meetings focused on support, expectations, delivery alignment, and general well-being. • 1:1 meetings • Manage the big-picture program timeline (releases, phases, go-live plans) using engineering velocity and capacity inputs provided by the Engagement Manager. • big-picture program timeline • Co-own delivery planning by managing the program-level timeline and facilitating Sprint Planning sessions ensuring release milestones and sprint goals are aligned with business priorities, clearly defined requirements, and the engineering capacity and feasibility inputs provided by the Engagement Manager. • Own overall delivery to ensure quality, scope, and timelines are consistently met. • Own overall delivery • Ensure robust business-facing documentation, including requirements documents, BRDs/PRDs, implementation plans, product roadmaps, and client-facing marketing or enablement materials. • business-facing documentation • Ensure delivery decisions reflect an understanding of operational costs, ROI, and long-term business impact. • costs, ROI, and long-term business impact • Identify delivery risks, create proactive mitigation plans, and track program health across all milestones with keen insight. • Identify delivery risks • Acknowledge new client requests promptly and partner with the Engagement Manager to assess feasibility, capacity, and timeline impact before providing any commitment to the client. • Acknowledge new client requests promptly and partner with the Engagement Manager to assess
Benefits
• Values • Values • We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value. • We are client first • We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection • We work at Start-Up Speed: • We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity. • We are AI forward: • Advantages of joining Turing • Amazing work culture (Super collaborative & supportive work environment; 5 days a week) • Awesome colleagues (Surround yourself with top talent from Meta, Google, LinkedIn etc. as well as people with deep startup experience) • Flexible working hours
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