Lead AI/ML Engineer (P4363)

LEAD AI/ML ENGINEER (P4363)

RESPONSIBILITIES
· Serve as the technical engineering lead on complex, multi-month projects to build and maintain agentic AI systems across organizational domains.
· Foster a collaborative and innovative team environment, encouraging professional growth and development among junior team members.
· Leverage established software engineering patterns, frameworks, and tools to automate, deploy, and integrate generative AI and agentic solutions.
· Develop tools, processes, and operational capabilities to monitor agent behavior, response quality, data accuracy, latency, and system reliability.
· Partner with researchers, domain experts, and platform teams to optimize and scale agentic systems using software engineering, DevOps, and AI lifecycle best practices.
· Package agent capabilities, tools, and supporting services as reusable APIs and components that can be integrated across the organization.
· Build, steward, and maintain production-grade agentic systems and supporting backend services, including agent APIs, asynchronous jobs, conversation state, file storage, and enterprise integrations, with a focus on security, reliability, observability, scalability, and performance.
· Evaluate emerging generative AI and agent frameworks, orchestration patterns, protocols, and tooling to identify opportunities for enterprise adoption and evolve existing systems.
· Translate business requirements into consumable agent capabilities and services, balancing scale, risk, response quality, latency, and cost to maximize value.
· Reduce time to delivery through automated build, test, and deployment pipelines, coordinated service releases, continuous evaluation, and user feedback and monitoring practices.
· Responsible for code reviews and QA sign off
· Maintain technical documentation, version control, deployment and rollback guidance, and knowledge-sharing practices to support collaboration and operational continuity.
· Make time-sensitive decisions and solve urgent problems without escalation.

QUALIFICATIONS, SKILLS, AND EXPERIENCE

Bachelor’s degree or higher in Computer Science, Computer Engineering, Software Engineering, or a related field.
· 4+ years of experience developing cloud-based software solutions and an understanding of design for scalability, performance, and reliability.
4+ years of experience applying programming languages, software design patterns, and modern engineering technologies to build production solutions.
2+ years of hands-on experience building and maintaining scalable AI-powered applications or agentic systems.
2+ years of experience with generative AI technologies preferred.
· 2+ years of experience in tech consulting, retail or related professional services preferred
Hands-on experience across the end-to-end software development lifecycle, using CI/CD, automated testing, and DevOps practices to develop, deploy, and maintain scalable solutions.
Experience delivering production-grade software services or agentic solutions that meet stakeholder needs.
· Excellent communication skills, particularly on technical topics.
· Strong time and project management skills; the ability to balance multiple, simultaneous work items and prioritize as necessary.
Knowledge of generative AI application patterns, including retrieval-augmented generation, tool use, and agent orchestration, is highly preferred.
Experience working with a managed agent platform, such as LangSmith, to support agent deployment, tracing, evaluation, and lifecycle management.
Experience working with AI-ready data, including preparing, validating, and organizing governed data and metadata for reliable retrieval, context grounding, and agent use.
Experience working with a managed data platform, such as Databricks or Snowflake.
· Strong skills in Python
Strong data engineering and data modeling skills, including integrating data sources and building reliable data pipelines.
· Kubernetes & Docker experience
· CI/CD Pipeline experience; Github Actions a plus
· Terraform experience
Experience writing agent evaluations is a plus.
Experience with prompt tuning is a plus.
Experience working with Model Context Protocol (MCP) servers is a plus.