Senior AI/ML Engineer

Role Overview

• Design, develop, and optimize AI and ML solutions to enhance operational and analytical capabilities within a larger enterprise level Data and AI analytics platform.
• Build and maintain end-to-end ML pipelines including data ingestion, feature engineering, model training, validation, and inference.
• Train and tune algorithms to improve predictive accuracy and decision-support tasks.
• Integrate AI/ML models into production environments using APIs, microservices, and DevSecOps pipelines.
• Support development and maintenance of model serving infrastructure and scalable inference capabilities.
• Implement model monitoring, performance evaluation, and drift detection mechanisms.
• Optimize model performance, scalability, and efficiency for production workloads.
• Collaborate with data engineers, data scientists, software developers, and DevSecOps teams to ensure alignment with enterprise architecture and security requirements.
• Support secure development and deployment of AI/ML models in compliance with enterprise cybersecurity requirements.
• Contribute to development of AI/ML artifacts including documentation, testing frameworks, and model evaluation reports.
• Support integration with enterprise AI/ML platforms and external model providers (e.g., cloud-based AI services).
• Ensure compliance with cybersecurity policies and standards throughout the project lifecycle.
• Stay updated on industry trends and advancements in AI/ML technologies.
• Identify and resolve technical challenges related to model accuracy, scalability, and integration.
• Analyze system performance metrics and recommend improvements for efficiency and scalability.
• Identify and integrate appropriate COTS, government, and custom tools within established frameworks.
• Manage project timelines and deliverables, ensuring adherence to quality standards.
• Facilitate communication between technical teams and stakeholders to align project goals.
• Develop and implement best practices for model development and deployment.

Requirements

• Active Secret clearance
• Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, Engineering, or related technical discipline and 8–12 years of relevant experience OR Master’s degree in a related field and 6–10 years of relevant experience
• Minimum of 6 years of experience in AI/ML and/or data intelligence engineering or related fields
• Experience developing and deploying machine learning models in enterprise environments
• Experience with programming languages such as Python, R, or Java and ML frameworks such as PyTorch, TensorFlow, or Scikit-learn
• Experience building and maintaining ML pipelines and data processing workflows
• Experience deploying models in containerized environments (Docker, Kubernetes)
• Experience integrating AI/ML solutions into APIs and microservices architectures
• Experience implementing model evaluation, performance tuning, and lifecycle management practices
• Experience with data pipeline construction and management
• Strong problem-solving abilities and analytical thinking
• Strong communication and interpersonal skills
• Experience in at least 2 of the following: Developing Agentic AI solutions, including autonomous planning–execution–reflection loops, multi-agent collaboration and coordination, and tool usage patterns including API integration, retrieval-augmented generation (RAG), and memory/context management
• Generative AI models including prompt engineering, chain-of-thought reasoning, and Natural Language Processing (NLP) tasks such as entity extraction, summarization, and semantic search.
• Large Language Models (LLMs) and agent frameworks such as LangChain, LangGraph, CrewAI, A2A, MCP, or AutoGen
• Vector databases (e.g., Pinecone, Weaviate, FAISS)
• Deployment into virtualized and containerized environments (e.g., VMware, Docker, Kubernetes)

Tech Stack

• Cloud
• Cyber Security
• Docker
• Java
• Kubernetes
• Microservices
• Python
• PyTorch
• Scikit-Learn
• Tensorflow
• VMware

Benefits

• Health and Wellness programs
• Income Protection
• Paid Leave
• Retirement