Artificial Intelligence Engineer

Key Responsibilities

GenAI Development:

Exposure to LLMs:

AG based application:

Cloud Deployment:

AI Agent Development:

Data Analytics

Data Visualization:

Stakeholder Collaboration:

Model Optimization:

LLMOps Best Practices: Implement GenAI-specific MLOps, including CI/CD pipelines (Git, Azure DevOps)

Leadership: Guide cross-functional teams, mentor junior engineers, and drive project execution with strategic vision and ownership.

Helicopters, strategic Thinking: Develop innovative GenAI strategies to address business challenges, leveraging data insights to align solutions with organizational goals.

Self-Driven Execution: Independently lead projects to completion with minimal supervision, proactively resolving challenges and seeking collaboration when needed.

Continuous Learning: Stay ahead of GenAI, analytics, and visualization advancements, self-learning new techniques to enhance project outcomes.

Required Skills & Experience

8-12 years in AI/ML development, with at least 4 years focused on Generative AI and AI agent frameworks.

Education: BTech/BE in Computer Science, Engineering, or equivalent (Masters or PhD in AI/ML is a plus).

Programming:

Mac Data Analytics:

Data Visualization:

Azure Cloud Expertise:

GenAI Methodologies

Deployment

LLMOps:

Communication: Exceptional verbal and written skills to articulate complex GenAI concepts, analytics, and visualizations to technical and non-technical stakeholders.

Strategic Thinking:

Problem-Solving

Collaboration: Collaborative mindset to work effectively across departments and engage colleagues for solutions when needed.

Preferred Skills

Experience deploying GenAI models in production environments, preferably on Azure Familiarity with multi-agent systems, reinforcement learning, or distributed training (e.g., DeepSpeek).

Knowledge of DevOps practices, including Git, CI/CD, and infrastructure-as-code. Advanced data analytics techniques (e.g., time-series analysis, A/B testing) for GenAI applications.

Experience with interactive visualization frameworks (e.g., Dash, Streamlit) for real-time dashboards.

Contributions to GenAI or data analytics open-source projects or publications in NLP, generative modeling, or data science