About the Role :

We are looking for a senior Artificial Intelligence Engineer with extensive experience in designing and architecting enterprise-scale AI/ML platforms and solutions. The ideal candidate should combine strong hands-on expertise across Generative AI, LLMs, Agentic AI, Machine Learning, distributed systems, and MLOps with the ability to lead technical architecture and engineering initiatives.

Responsibilities :

- Architect and develop enterprise-scale AI/ML platforms and solutions spanning Generative AI, predictive AI, real-time analytics, and machine learning.

- Design and implement solutions using LLMs, RAG, Agentic AI, LangGraph, prompt engineering, and fine-tuning techniques.

- Lead the design and deployment of AI solutions across the complete lifecycle, from experimentation and prototyping to production deployment and monitoring.

- Develop and optimize machine learning and deep learning models for predictive analytics, fraud detection, and other enterprise use cases.

- Design real-time AI and decisioning solutions with strong focus on feature engineering, low-latency inference, scalability, and reliability.

- Architect distributed data and AI processing solutions using technologies such as Apache Spark, Kafka, and Flink.

- Design scalable ML infrastructure and orchestration using Kubernetes, Kubeflow, MLflow, and Ray.

- Drive MLOps and LLMOps practices covering model lifecycle management, deployment, monitoring, evaluation, versioning, and continuous improvement.

- Optimize AI workloads for performance and cost, including GPU utilization, distributed training/inference, and model optimization.

- Collaborate with data engineering, software engineering, cloud, product, security, and business teams to translate complex requirements into scalable AI solutions.

- Define architecture standards, technology choices, engineering practices, and reusable components for AI platforms.

- Evaluate emerging AI technologies, frameworks, and models and identify opportunities to incorporate them into enterprise solutions.

- Ensure AI platforms and solutions meet requirements for security, scalability, reliability, governance, responsible AI, and data privacy.

- Provide technical leadership and mentorship to AI/ML engineers and data scientists.

- Lead architecture reviews, technical discussions, PoCs, and enterprise AI transformation initiatives.

Requirements :

- 13+ years of experience in Artificial Intelligence, Machine Learning, Data Science, Software Engineering, or related technology domains.

- Strong experience designing and architecting enterprise-scale AI/ML platforms and solutions.

- Deep hands-on expertise in Generative AI, LLMs, RAG, Agentic AI, LangGraph, and model fine-tuning.

- Strong foundation in Machine Learning, Deep Learning, predictive modeling, and statistical techniques.

- Experience building real-time AI, fraud detection, or low-latency decisioning solutions is preferred.

- Strong proficiency in Python and experience with distributed computing frameworks such as Spark, Kafka, and Flink.

- Hands-on experience with Kubernetes, Kubeflow, MLflow, and/or Ray.

- Strong understanding of MLOps/LLMOps, including model deployment, monitoring, evaluation, versioning, and production lifecycle management.

- Experience with GPU optimization, distributed training/inference, or AI workload optimization.

- Strong understanding of cloud-based AI/ML infrastructure and modern distributed systems.

- Experience designing scalable, highly available, and secure AI platforms for enterprise environments.

- Strong understanding of AI security, governance, responsible AI, and data privacy considerations.

- Proven ability to lead technical teams, mentor engineers, and influence architecture and technology decisions.

- Excellent communication, stakeholder management, problem-solving, and technical leadership skills.