Artificial Intelligence Engineer
First seen by Alion on Sep 11, 2026.
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.
Skills
Generative AI, Machine Learning, Python, Apache Spark, Kafka, MLOps, LangGraph, Kubeflow, Artificial Intelligence, LLM