Senior AI Engineer (PhD)

Overview

Key Responsibilitiesai Research & Innovation

• Conduct advanced research in Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI.
• Evaluate emerging AI technologies, frameworks, and methodologies to enhance organizational capabilities.
• Publish research findings, technical papers, patents, or contribute to industry thought leadership initiatives.
• Lead proof-of-concept (PoC) development for innovative AI solutions.

Solution Design & Development

• Architect and develop scalable AI/ML models and intelligent systems.
• Design and optimize Large Language Model (LLM)-based applications and AI agents.
• Build and fine-tune machine learning and deep learning models for production environments.
• Develop AI-powered applications involving NLP, Computer Vision, Predictive Analytics, and Recommendation Systems.
• Integrate AI solutions with enterprise platforms, APIs, and cloud environments.

Generative Ai & Llm Engineering

• Develop and optimize Retrieval-Augmented Generation (RAG) architectures.
• Fine-tune foundation models and implement prompt engineering strategies.
• Design AI agents, autonomous workflows, and conversational AI solutions.
• Implement vector databases and semantic search frameworks.
• Evaluate model performance, accuracy, safety, and responsible AI practices.

Mlops & Deployment

• Build end-to-end ML pipelines for model training, deployment, monitoring, and maintenance.
• Implement CI/CD processes for AI applications.
• Optimize model performance, scalability, and infrastructure costs.
• Monitor model drift and ensure continuous improvement.

Leadership & Collaboration

• Provide technical leadership and mentorship to AI Engineers, Data Scientists, and ML Engineers.
• Collaborate with business stakeholders to identify AI opportunities and define solution roadmaps.
• Participate in strategic technology planning and AI transformation initiatives.
• Review code, architecture, and technical deliverables to ensure quality standards.

Required Qualificationseducation

• PhD in Artificial Intelligence, Machine Learning, Computer Science, Data Science, Mathematics, Statistics, or a related discipline.

Experience

• 8+ years of experience in AI/ML development, including research and production deployment.
• Proven experience leading AI projects in enterprise environments.
• Experience working with Generative AI, LLMs, and advanced deep learning architectures.

Technical Skillsprogramming

• Python (Advanced)
• SQL
• Java, C++, or Scala (Preferred)

Ai And Ml Frameworks

• PyTorch
• TensorFlow
• Keras
• Scikit-learn

Generative Ai & Llm Technologies

• OpenAI APIs
• LangChain
• LangGraph
• LlamaIndex
• RAG Architectures

Cloud Platforms

• AWS
• Microsoft Azure

Mlops & Devops

• Docker
• Kubernetes
• MLflow
• Kubeflow
• Jenkins
• GitHub Actions

Databases

• PostgreSQL
• MongoDB
• Vector Databases

Preferred Qualifications

• Publications in reputed journals or AI conferences (NeurIPS, ICML, ICLR, ACL, CVPR, etc.).
• Experience in Agentic AI, Autonomous Systems, and Multi-Agent Frameworks.
• Knowledge of AI Governance, Responsible AI, Explainable AI (XAI), and AI Security.
• Industry certifications in Cloud, AI, or Machine Learning technologies.

Core Competencies

• Strategic Thinking

• Research & Innovation

• Problem Solving

• Leadership & Team Mentoring

• Stakeholder Management

• Communication & Presentation Skills

• Analytical Thinking
• Project Management

Key Deliverables

• Production-ready AI and Generative AI solutions.
• Scalable machine learning pipelines and architectures.
• AI research initiatives and innovation roadmaps.
• Performance optimization and continuous model improvement.
• Technical mentorship and knowledge-sharing across teams.

Education:** PhD (Mandatory)

Education

• Master's (Preferred)

Experience

• Senior Engineer: 10 years (Required)