Lead, Artificial Intelligence

Job Title: Lead, Artificial Intelligence

Job Code: 44964

Job Location: Melbourne, FL

Job Schedule: 9/80

Job Description:

L3Harris seeks a Technical Leader to advance the implementation of Mission Networks Artificial Intelligence (AI) initiatives, translating strategic goals into secure scalable and measurable solutions. The successful candidate will serve as an AI Subject Matter Expert (SME) for the organization, provide guidance to the teams implementing AI solutions, and help grow the internal AI capabilities through hands-on mentorship and technical leadership. Design systems using AI for high-assurance operational environments delivering modern, secure, reliable and resilient telecommunications networks and information management systems for Air Traffic Management (ATM) and Surveillance.

·Lead the technical delivery of AI initiatives from concept through deployment, operations, and continuous improvement.

·Define AI architecture, technical standards, reusable patterns, and best practices aligned to business and mission needs.

·Provide expert guidance to AI teams and stakeholders on technology selection, solution design, risk management, and implementation priorities.

·Help establish practical AI governance for responsible, secure, compliant, and trustworthy use of AI.

·Mentor engineers, data professionals, and business teams to strengthen AI knowledge, delivery discipline, and innovation across the organization.

·The role is both a technical leader and a trusted advisor where the candidate will mentor engineers, drive architectural designs and solutions, and act as the go-to SME for AI.

Essential Functions:

· AI/ML and Generative AI: Deep knowledge of machine learning, large language models (LLM), retrieval-augmented generation (RAG), agentic workflows, model evaluation, and production AI solution patterns.

·AI Architecture and MLOps: Experience designing scalable AI platforms, data/model pipelines, model deployment, monitoring, observability, and lifecycle management.

·Responsible AI and Governance: Ability to apply standards and best practices for security, privacy, explainability, bias mitigation, model risk management, and regulatory compliance.

·AI Technology Strategy: Ability to assess emerging tools, platforms, frameworks, and vendors; establish practical roadmaps that connect AI investments to organizational outcomes. Establish partnerships and relationships with vendors providing AI technology to include in successful solutions.

·Technical Leadership and Mentorship: Proven ability to lead multidisciplinary teams, communicate complex AI concepts clearly, establish engineering standards, and develop internal AI talent.

Qualifications:

·Bachelor’s Degree and minimum 9 years of prior relevant experience. Graduate Degree and a minimum of 7 years of prior related experience.

·3+ years of design & implementation of successful solutions using AI technologies

·3+ years of experience with machine learning, LLM’s, RAG and AI solutions

·3+ years of experience with Differentiable Programming libraries (e.g., PyTorch, TensorFlow) and Database Manipulation libraries (e.g., SQL, NoSQL, Pandas, PySpark)

·Ability to obtain and maintain an FAA public trust or Top-Secret clearance

Preferred Additional Skills:

·Experience with cloud AI services (e.g., AWS SageMaker/Bedrock, Google Vertex AI, Azure AI, Azure OpenAI Service, or Databricks)

·Hands-on use of LLM orchestration frameworks and tools (e.g., LangChain, LlamaIndex, Semantic Kernel, other agent-development frameworks)

·Hands-on use of retrieval-augmented generation (RAG), vector databases, embeddings, knowledge graphs, and search platforms

·Experience developing, fine-tuning, evaluating, or operating large language models and open-source models

·Experience with MLOps and LLMOps tooling (e.g., MLflow, Kubeflow, SageMaker, Docker, Kubernetes, CI/CD, or model monitoring platforms)

·Experience with AI model evaluation and prompt engineering

·Knowledge of responsible AI frameworks and emerging AI regulations

·Experience integrating AI capabilities into enterprise applications through APIs, microservices, event-driven architectures, or workflow automation tools