Accountabilities:

Own the technical success of enterprise customers' agentic AI initiatives, managing the complete delivery lifecycle from initial discovery and solution design through development, production deployment, ongoing operations, and advisory services.
Lead customer workshops to identify, assess, and prioritize high-value agentic AI use cases, translating business objectives into measurable and technically achievable workflows.
Design and implement sophisticated multi-agent architectures, including agent decomposition, orchestration, task delegation, tool integration, memory management, state handling, and human-in-the-loop controls.
Develop, review, and troubleshoot production-quality software using Go or Python, building agents, plugins, tools, and integrations that meet enterprise requirements.
Deploy and operate agentic AI applications using Docker, Kubernetes, and major cloud platforms, ensuring scalability, reliability, and maintainability.
Integrate AI agents with enterprise applications, data sources, and legacy systems through MCP servers, A2A protocols, REST APIs, and event-driven messaging architectures.
Implement retrieval-augmented generation (RAG), vector databases, knowledge bases, embeddings, and real-time event context strategies to improve agent accuracy and access to relevant information.
Apply the Agent Development Lifecycle (ADLC) throughout solution delivery, covering requirements gathering, architecture design, evaluation pipelines, testing, version control, release management, monitoring, and operational documentation.
Establish robust observability, security controls, governance frameworks, and responsible AI practices for agentic systems, including tracing, quality evaluation, cost and latency monitoring, access control, and auditability.
Advise customers on large language model selection and optimization across providers such as Anthropic, OpenAI, Google, AWS Bedrock, and Azure AI Foundry, considering prompt engineering, token consumption, costs, and model performance trade-offs.
Act as a long-term technical advisor, identifying opportunities for further AI adoption and helping customers progress from isolated pilots to enterprise-wide, event-driven AI implementations.
Conduct recurring technical reviews, solution health checks, and roadmap discussions aligned with customer priorities and annual planning cycles.
Enable customer engineering teams to independently operate, maintain, and extend implemented solutions through technical workshops, collaborative development sessions, and comprehensive documentation.
Develop reusable technical assets, including reference architectures, implementation patterns, accelerators, and best practices, to improve delivery efficiency and consistency across future engagements.
Represent customer needs and technical feedback to internal Product and Engineering teams, contributing insights that influence product development and roadmap priorities.
Collaborate closely with agentic AI platform engineering teams to resolve complex customer challenges, validate new capabilities, and contribute reusable agents, plugins, and accelerators.
Provide technical leadership and mentorship to other professionals, helping strengthen agentic AI expertise and delivery capabilities across the Professional Services organization.
Communicate complex technical concepts, AI-related risks, and architectural trade-offs clearly to both technical and non-technical stakeholders.
Travel when required to support customer engagements, including occasional international travel.

Requirements

At least 7 years of professional experience in professional services, technology consulting, systems integration, forward-deployed engineering, or enterprise application development.
Minimum 2 years of hands-on experience designing, developing, and deploying LLM-based applications and agentic AI solutions, including at least one solution successfully taken into production.
Strong understanding of software development lifecycle (SDLC) practices, including version control, automated testing, CI/CD, and release management, with practical experience applying a structured Agent Development Lifecycle (ADLC).

How Jobgether works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

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