Lead AI Engineer

Lead AI Engineer (6-10 Years Experience)

Position Summary

The Lead Agentic AI Engineer will lead the architecture and implementation of complex Agentic AI ecosystems, and autonomous business workflow solutions. This role combines deep technical leadership with hands-on engineering expertise and client engagement.

Key Responsibilities

• Architect enterprise-scale Agentic AI solutions.
• Lead development of multi-agent orchestration platforms.
• Build governance, observability, and security frameworks.
• Establish standards for agent lifecycle management.
• Lead POCs, MVPs, and production deployments.
• Review architecture and code quality.
• Mentor team of engineers
• Work closely with clients to define AI transformation roadmaps.
• Drive innovation and identify emerging AI technologies.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, AI, or related discipline.
• 6-10 years of software engineering experience.
• 2+ years in AI/ML or Generative AI.
• Demonstrated experience delivering enterprise AI solutions.

Technical Expertise

• Advanced proficiency in distributed systems along with Event-driven architecture
• Experience and expertise in Microservices and containerization (Kubernetes)
• Advanced proficiency in programming languages such as Python and Java.
• Strong knowledge of machine learning frameworks
• Experience with cloud services (e.g., AWS, Azure, Google Cloud) for AI applications.
• Solid understanding of data processing and analysis tools (e.g., Pandas, NumPy).
• Familiarity with natural language processing (NLP) and computer vision techniques.
• Excellent problem-solving skills and the ability to work in a collaborative environment.
• Strong communication skills, with the ability to present complex concepts to non-technical stakeholders.
• Experience with version control systems (e.g., Git) and agile methodologies.

Leadership Expectations

• Lead technical design workshops with clients and internal teams to translate business goals into scalable Agentic AI solution architectures.
• Provide technical oversight across projects, ensuring architecture consistency, engineering quality, delivery predictability, and alignment with enterprise AI standards.
• Mentor and grow engineering talent by coaching teams on AI engineering practices, solution design, code quality, and production-readiness principles.
• Influence AI best practices across the organization by defining reusable patterns, governance guidelines, delivery accelerators, and responsible AI adoption approaches.

Nice to have

• Experience/exposure to Multi-agent architectures
• Experience/exposure to Agent orchestration
• Experience/exposure to Autonomous workflows
• Experience/exposure to Agent memory systems
• Experience/exposure to AI governance and guardrails

Roles and Responsibilities

Required Qualifications

• Bachelor's or Master's degree in Computer Science, AI, or related discipline.
• 6-10 years of software engineering experience.
• 2+ years in AI/ML or Generative AI.
• Demonstrated experience delivering enterprise AI solutions.

Technical Expertise

• Advanced proficiency in distributed systems along with Event-driven architecture
• Experience and expertise in Microservices and containerization (Kubernetes)
• Advanced proficiency in programming languages such as Python and Java.
• Strong knowledge of machine learning frameworks
• Experience with cloud services (e.g., AWS, Azure, Google Cloud) for AI applications.
• Solid understanding of data processing and analysis tools (e.g., Pandas, NumPy).
• Familiarity with natural language processing (NLP) and computer vision techniques.
• Excellent problem-solving skills and the ability to work in a collaborative environment.
• Strong communication skills, with the ability to present complex concepts to non-technical stakeholders.
• Experience with version control systems (e.g., Git) and agile methodologies.

Leadership Expectations

• Lead technical design workshops with clients and internal teams to translate business goals into scalable Agentic AI solution architectures.
• Provide technical oversight across projects, ensuring architecture consistency, engineering quality, delivery predictability, and alignment with enterprise AI standards.
• Mentor and grow engineering talent by coaching teams on AI engineering practices, solution design, code quality, and production-readiness principles.
• Influence AI best practices across the organization by defining reusable patterns, governance guidelines, delivery accelerators, and responsible AI adoption approaches.

Nice to have

• Experience/exposure to Multi-agent architectures
• Experience/exposure to Agent orchestration
• Experience/exposure to Autonomous workflows
• Experience/exposure to Agent memory systems
• Experience/exposure to AI governance and guardrails