Lead AI/ML Engineer (Generative AI Agent Systems)
J
JobgetherAI/ML Technology
IndiaFull-TimeLead
Salary not disclosed
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Job Details
- Required Skills
- GoGenerative AIDistributed Systems
Requirements
- Extensive experience in AI/ML engineering, with significant expertise in Generative AI, large language models, autonomous agents, and multi-agent systems.
- Proven background in professional services, consulting, or client-facing technology leadership roles, engaging senior stakeholders like CTOs.
- Deep hands-on experience designing and deploying production-grade AI agent systems, reasoning frameworks, and LLM-powered workflows.
- Expert-level knowledge of the Google Cloud ecosystem, particularly Vertex AI services, including Agent Development Kit (ADK), Agent Engine, Vector Search, and model tuning.
- Strong expertise in enterprise cloud security architectures, including identity management, network security, and secure AI deployment frameworks.
- Advanced software engineering capabilities with strong proficiency in Go (Golang).
- Experience building distributed, highly concurrent, and event-driven systems.
- Solid understanding of AI governance, model evaluation methodologies, observability, monitoring, and responsible AI practices.
- Experience operating in regulated industries such as healthcare, financial services, or public sector environments with strict compliance and auditing requirements.
- Strong system design, analytical, and problem-solving skills, with the ability to balance innovation, scalability, and operational resilience.
- Excellent communication, leadership, and stakeholder management skills.
- Demonstrated ability to lead cross-functional teams, establish technical standards, and drive strategic AI initiatives.
Responsibilities
- Serve as the primary subject matter expert for Generative AI, large language models, reasoning frameworks, and autonomous agent ecosystems across enterprise engagements.
- Lead technical discovery sessions and collaborate with senior client stakeholders to translate business requirements into scalable AI architectures and implementation roadmaps.
- Define and establish reusable delivery frameworks, playbooks, and best practices for deploying agentic AI solutions.
- Design and own the technical vision for production-ready agent architectures, leveraging modern AI development frameworks and cloud-native technologies.
- Lead the specification and implementation of AI agents, including function calling frameworks, prompt engineering methodologies, context management, and data interaction patterns.
- Define autonomy models and human-in-the-loop (HITL) strategies to ensure appropriate governance, oversight, and operational reliability.
- Establish distributed systems principles, including event-driven architectures, idempotent processing strategies, and scalable integration patterns.
- Design and oversee continuous evaluation frameworks, shadow-mode testing environments, and validation processes.
- Advise stakeholders on AI governance, model lifecycle management, version control, compliance requirements, and drift monitoring strategies.
- Lead proof-of-concept initiatives and accelerate the adoption of enterprise-grade AI agent solutions through hands-on technical leadership and mentorship.
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