AI Security Engineer - Consultant

We are seeking a experienced AI/ML Engineer with 1-3 years of experience to lead the design and development of enterprise-scale AI platforms, including agentic AI systems and generative AI solutions.

This role requires deep expertise in LLM architecture, AI product development, and advanced analytics, combined with strong engineering foundations in cloud, MLOps, and distributed systems. You will drive end-to-end AI strategy from research and prototyping to production deployment while ensuring responsible and governed AI adoption.

Key Responsibilities

AI GenAI Architecture

• Design and implement scalable AI platforms supporting agentic AI systems and autonomous workflows
• Architect and optimize LLM-based systems, including fine-tuning, inference, and orchestration
• Build advanced RAG (Retrieval-Augmented Generation) pipelines and multi-agent systems
• Develop multimodal AI systems (text, image, audio) for enterprise use cases
• Lead AI product development from concept to production deployment

Machine Learning Advanced Analytics

• Develop and deploy predictive models and advanced analytics solutions
• Design scalable ML systems for real-time and batch processing
• Implement optimization techniques such as PEFT, LoRA, QLoRA, and mixed precision training
• Apply reinforcement learning approaches including RLHF (Reinforcement Learning with Human Feedback)
• Ensure robustness using frameworks like RAG Triad (retrieval, augmentation, generation evaluation)

Architecture Engineering

• Design systems using microservices and hexagonal architecture patterns
• Build and maintain scalable data pipelines and API integrations
• Ensure seamless integration between AI services and enterprise platforms
• Lead system design reviews and ensure high availability, scalability, and security

MLOps Platform Engineering

• Implement end-to-end MLOps pipelines, including CI/CD for ML systems
• Deploy and manage models using Kubernetes and Docker
• Establish model monitoring, drift detection, and performance tracking
• Automate model lifecycle management and continuous retraining workflows

Cloud Data Platforms

• Architect and manage AI workloads across cloud platforms:
• AWS (S3, SageMaker, Redshift, Glue)
• GCP and Azure ecosystems
• Work with modern data platforms such as Databricks and Cosmos DB
• Optimize large-scale data processing and storage for AI workloads

Responsible AI Governance

• Define and implement AI governance frameworks
• Ensure compliance with responsible AI principles (fairness, explainability, transparency)
• Implement guardrails and safety mechanisms for LLM systems
• Align AI systems with enterprise risk, compliance, and regulatory requirements

Required Skills Qualifications

• 1-3+ years of experience in AI/ML engineering, architecture, or data science
• Strong expertise in Generative AI and LLM architectures
• Proven experience building AI products at scale
• Deep understanding of agentic AI systems and autonomous agents
• Expertise in advanced analytics and predictive modeling

Technical Expertise

• AI frameworks: LangChain, LangGraph, RAG, RLHF, multimodal AI
• LLM optimization: PEFT, LoRA, QLoRA, mixed precision
• Strong experience with Python and ML frameworks (PyTorch, TensorFlow)
• MLOps: CI/CD pipelines, model monitoring, lifecycle management
• Containers orchestration: Kubernetes, Docker
• Architecture: microservices, hexagonal architecture

Cloud Data Expertise

• Strong experience with:
• AWS (S3, SageMaker, Redshift, Glue)
• GCP and Azure
• Databricks, Cosmos DB
• Experience designing large-scale data pipelines and distributed systems

Preferred Qualifications

• Experience leading AI/ML teams or large-scale transformation programs
• Strong background in AI platform engineering
• Experience with multi-agent orchestration frameworks
• Knowledge of AI security and adversarial ML
• Contributions to AI research, patents, or open-source projects

Certification Stack

• Google ML Engineer or AWS ML Specialty
• Kubernetes (CKA)
• DevOps certification