Lead AI Engineer

6-8 Years

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Early Applicant

Job Description

Your Role and Impact

Architect & Design scalable enterprise-grade GenAI and Agentic AI solutions using SLMs, Crew.AI, and multi-agent orchestration.
Implement AI governance layer model monitoring, bias detection, audit trails, explainability, and security controls.
Lead technical project management requirement gathering, sprint planning, backlog prioritization, and delivery ownership.
Hands-on development of LLMOps/MLOps pipelines, CI/CD for AI models, and scalable deployment on Azure / hybrid environments.
Build and optimize SLM-based systems ensuring performance, scalability, and cost efficiency.
Stay updated with emerging AI trends, research, and governance frameworks, driving POCs and innovation adoption.
Ensure compliance with Responsible AI principles, data privacy, and enterprise security standards.
Mentor and guide teams on GenAI, governance practices, and system design.

Key Skills

Strong expertise in Prompt Engineering, SLMs, Crew.AI, Agentic AI frameworks.
Proven hands-on experience with Docker, Kubernetes, cloud/on-prem deployments (Azure preferred).
Knowledge of governance frameworks for AI (bias mitigation, monitoring, compliance).
Experience in LLMOps/MLOps, CI/CD pipelines, model lifecycle management.
Strong understanding of system design, scalability, and performance optimization.
Technical project management and team leadership capabilities.
Research-driven mindset with ability to evaluate and adopt new AI trends.

Your Contribution

Role: Lead GenAI & Agentic AI Architect / Technical Project Manager (68 Years)Key Responsibilities

Architect and implement enterprise-grade GenAI & Agentic AI solutions using SLMs, Crew.AI, Prompt Engineering, and multi-agent orchestration.
Design scalable and secure cloud/on-prem systems leveraging Azure Stack, Kubernetes, Docker, and distributed AI pipelines.

Implement AI governance frameworks including model monitoring, auditability, bias detection, explainability, and security controls.
Establish guardrails and policy layers for safe deployment of Agentic AI systems in enterprise environments.
Ensure compliance with data privacy, responsible AI, and regulatory standards.

Lead cross-functional execution, driving AI/GenAI projects from requirement gathering to delivery with clear technical roadmaps.
Manage sprints, prioritize backlog, and ensure timely delivery of milestones.

Build and deploy LLMOps/MLOps pipelines for experimentation, versioning, CI/CD of models, and continuous monitoring.
Lead hands-on development of AI agents, integrating with enterprise APIs and applications.

Track and evaluate latest advancements in GenAI, Agentic AI, governance frameworks, and SLM optimization.
Conduct POCs and benchmarking to assess new technologies for enterprise adoption.