AI / ML Assistant Manager

We are seeking a highly experienced Principal AI/ML Architect to lead the design, development, and deployment of enterprise-scale AI platforms, including Generative AI, Agentic AI, and advanced machine learning solutions. The ideal candidate will have deep expertise in large language models (LLMs), AI platform engineering, MLOps, cloud ecosystems, and scalable distributed systems.

This role requires strong technical leadership to drive AI innovation, architect robust AI systems, and enable responsible AI adoption across enterprise environments.

• Design and implement scalable AI platforms supporting agentic AI systems and autonomous workflows.
• Architect and optimize LLM-based applications, including model fine-tuning, inference optimization, and orchestration.
• Build advanced Retrieval-Augmented Generation (RAG) pipelines and multi-agent AI systems.
• Develop multimodal AI solutions involving text, image, and audio processing.
• Lead end-to-end AI product development from concept to production deployment.
• Define enterprise AI architecture standards and best practices.

• Develop and deploy predictive analytics and machine learning solutions at scale.
• Design scalable ML systems for both real-time and batch processing workloads.
• Implement optimization techniques such as PEFT, LoRA, QLoRA, and mixed precision training.
• Apply reinforcement learning approaches including RLHF (Reinforcement Learning with Human Feedback).
• Improve AI model robustness, evaluation, and performance monitoring frameworks.

• Design systems using microservices and hexagonal architecture principles.
• Build scalable APIs, data pipelines, and integration frameworks.
• Ensure seamless integration between AI services and enterprise platforms.
• Conduct architecture reviews and ensure scalability, reliability, security, and high availability.

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

• Architect and manage AI workloads across AWS, Azure, and GCP ecosystems.
• Work with modern data platforms including Databricks and Cosmos DB.
• Design scalable distributed data processing and storage architectures.
• Optimize infrastructure for high-performance AI workloads.

• Define and implement AI governance frameworks and enterprise AI guardrails.
• Ensure compliance with responsible AI principles including fairness, explainability, transparency, and accountability.
• Implement AI safety mechanisms and risk mitigation strategies for LLM systems.
• Align AI systems with enterprise compliance, security, and regulatory requirements.

• 12+ years of experience in AI/ML engineering, architecture, or data science.
• Strong expertise in Generative AI and Large Language Model (LLM) architectures.
• Proven experience building and deploying AI products at enterprise scale.
• Deep understanding of agentic AI systems and autonomous agents.
• Expertise in advanced analytics, predictive modeling, and distributed AI systems.
• Strong programming skills in Python.
• Hands-on experience with PyTorch, TensorFlow, and modern ML frameworks.
• Experience with LangChain, LangGraph, RAG frameworks, RLHF, and multimodal AI systems.
• Strong understanding of MLOps, CI/CD pipelines, model monitoring, and lifecycle management.
• Expertise in Kubernetes, Docker, and container orchestration technologies.
• Experience designing microservices-based and distributed architectures.
• Strong experience with cloud platforms including AWS, Azure, and GCP.
• Hands-on expertise with services such as SageMaker, S3, Redshift, Glue, Databricks, and Cosmos DB.
• Strong understanding of enterprise integration patterns and scalable system design.

• Experience leading AI/ML teams or enterprise-scale transformation initiatives.
• Strong background in AI platform engineering and multi-agent orchestration frameworks.
• Knowledge of AI security, adversarial machine learning, and LLM security frameworks.
• Experience contributing to AI research, patents, publications, or open-source projects.
• Exposure to enterprise governance, compliance, and AI risk management frameworks.
• Experience in highly regulated enterprise environments.

• Bachelors or Masters degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.