Technical Lead

e&e is seeking a Technical Lead for an onsite contract opportunity in Harrisburg, PA!

We are seeking an experienced Technical Lead to provide overall technical leadership, architecture ownership, and engineering direction for a complex enterprise technology environment. This is a highly hands-on leadership position requiring a senior engineer who has progressed into architecture and technical leadership while remaining actively involved in software development.

The Technical Lead will own the end-to-end architecture, establish and enforce engineering standards, guide distributed and contracted engineering teams, and ensure that solutions meet rigorous quality, security, performance, and operational requirements. A major focus of the position is the design and delivery of production AI-enabled and agentic systems, including agent harnesses, multi-step workflows, context and tool engineering, durable state, evaluation, and verification. The role will also serve as a senior technical partner to client engineering, infrastructure, data, and security teams.

Key Responsibilities

• Own the end-to-end technical architecture for enterprise platforms, applications, services, integrations, and supporting infrastructure.
• Define architectural decisions, document technical trade-offs, and establish scalable patterns that can be effectively maintained by distributed and rotating engineering teams.
• Remain hands-on with development by writing and reviewing code, creating prototypes and reference implementations, and leading the most technically challenging portions of projects.
• Establish and enforce engineering standards through automated quality controls, including builds, testing, code coverage, static analysis, dependency reviews, secret scanning, and pull-request approvals.
• Provide technical leadership, design guidance, code reviews, and onboarding for contracted and distributed engineering teams.
• Design, build, and operate production AI-enabled and agentic systems, including agent harnesses, goals, loops, context management, tool interfaces, and verification mechanisms.
• Design multi-step AI workflows using explicit and inspectable graphs with durable state, checkpointing, retry and recovery capabilities, defined stopping conditions, and human approval gates.
• Develop and maintain effective context engineering, retrieval, embedding, vector search, structured-output, tool/function calling, and model integration strategies.
• Establish model-selection and data-handling standards based on workload requirements, security, cost, latency, reliability, and contractual constraints.
• Define clear acceptance criteria and verification standards before development begins and ensure completion claims are supported by measurable, automated evidence.
• Develop evaluation frameworks for AI systems that measure success rates, human intervention, reliability, and cost per completed task.
• Partner with engineering, infrastructure, data, and security teams on integrations, data flows, environments, deployment processes, and incident response.
• Lead security and privacy design for externally accessible systems and sensitive data, including authentication, authorization, least privilege, identity, credential management, and threat modeling.
• Address AI-specific security concerns such as prompt injection, untrusted inputs, privileged actions, and environments executing model-generated code.
• Own non-functional requirements including application performance, availability, scalability, operability, reliability, and operating costs.
• Provide senior engineering support during deployments, upgrades, production incidents, and other periods of significant operational change.
• Communicate technical decisions, architecture recommendations, risks, and incident findings clearly to both technical and non-technical stakeholders.
• Identify reusable architectural patterns, AI harnesses, tools, and engineering components that can be leveraged across future initiatives.

Requirements

• Extensive professional software engineering experience with a strong hands-on background in building, deploying, and supporting production applications.
• Demonstrated experience owning the architecture of complex production systems and making architectural decisions with long-term scalability and maintainability in mind.
• Strong expertise in .NET and C#, along with working knowledge of React and modern front-end development.
• Strong relational database and data-modeling experience with PostgreSQL and/or SQL Server.
• Python development experience within AI, machine learning, or related solutions.
• Strong cloud engineering experience with Microsoft Azure or a comparable enterprise cloud platform, including identity, networking, containerized workloads, capacity, and cost management.
• Experience with CI/CD, automated quality gates, GitHub Actions or equivalent technologies, and Infrastructure as Code.
• Direct production experience designing and building AI-enabled and agentic applications.
• Hands-on experience with agent harnesses, loop engineering, context engineering, prompt design, retrieval strategies, structured outputs, and tool/function interfaces.
• Experience with agent/tool interoperability technologies such as Model Context Protocol (MCP) or comparable approaches.
• Experience developing multi-step agentic workflows using explicit graphs, durable state, checkpointing, retry mechanisms, and human approval processes.
• Strong understanding of AI evaluation and verification practices, including deterministic validation, automated evaluation, observability, and human review.
• Strong software security background, including threat modeling, authentication, authorization, identity management, secrets management, and data protection.
• Understanding of AI-specific security risks and appropriate controls for production agentic systems.
• Proven ability to establish and enforce engineering standards across distributed, contracted, or cross-functional development teams.
• Strong technical leadership and client-facing communication skills, with the ability to explain complex technical decisions to both technical and business stakeholders.
• Ability to document architecture decisions, designs, technical findings, and incident outcomes clearly and thoroughly.
• Must be authorized to work in the United States.

Preferred Qualifications

• Experience within K–12 education, virtual education, or another environment involving highly sensitive or regulated data.
• Experience integrating learning management, student information, identity, reporting, or other enterprise platforms.
• Knowledge of student-data privacy requirements, including FERPA and COPPA.
• Experience implementing MCP servers/clients or similar AI tool interoperability solutions.
• Experience with evaluation, tracing, monitoring, and observability platforms for AI/model-based applications.
• Experience leading distributed engineering and vendor teams across multiple time zones.
• Previous consulting or client-facing technical leadership experience.
• Relevant cloud or architecture certifications, such as Microsoft Certified: Azure Solutions Architect Expert.