Senior AI/ML Engineer

Description

Think of TEKsystems Global Services (TGS) as the growth solution for enterprises today. We unleash growth through technology, strategy, design, execution and operations with a customer-first mindset for bold business leaders. We deliver cloud, data and customer experience solutions. Our partnerships with leading cloud, design and business intelligence platforms fuel our expertise.

We value deep relationships, dedication to serving others and inclusion. We drive positive outcomes for our people and our business, and we stay true to our commitments and act in harmony with our words. We exist to create significant opportunity for people to achieve fulfillment through career success.

Ready to join us?

Here’s what the opportunity supported through our TGS Talent Acquisition Team requires:

Position Overview

We are seeking a highly skilled and motivated Senior AI/ML Engineer with 5 or more years of experience in data engineering and at least 3 years in AI/ML engineering. The ideal candidate will have hands-on expertise in designing, developing, and deploying secure, scalable, and high-performance AI/ML pipelines that meet DoD mission requirements, ensuring full compliance with RMF, NIST, and CMMC frameworks. The ideal candidate should have proficiency in Microsoft Azure and either Amazon Web Service (AWS) or Google Cloud Platform (GCP) with a solid foundation in Machine Learning and MLOps, cloud-native tools, and data governance.

This is a 6-month contract assignment supporting our aerospace manufacturing client with the potential to transition into full-time employment with TEKsystems Global Services.

The location of this position is flexible and can operate fully remote within the United States.

Key Responsibilities

• Actively participate in whole AI/ML pipeline design, development, and implementation lifecycle.
• Conduct exploratory data analysis (EDA), data cleaning, and statistical validation aligned with DoD data assurance principles.
• Engineer feature pipelines and automate feature stores in Azure Feature Store or within Databricks.
• Build end-to-end ML pipelines with MLflow, Delta Lake, and Azure Machine Learning (AML) for training, evaluation, and model lifecycle management.
• Provide guidance on implementing AI agent frameworks such as LlamaIndex, crewAI, LangGraph, and Azure AI Foundry.
• Design, implement, and optimize AI solutions leveraging both Large Language Models (LLMs) and Small Language Models (SLMs), ensuring scalability, efficiency, and alignment with business objectives.
• Develop and deploy scalable Machine Learning models using Azure Databricks and Lakehouse architecture.
• Enforce security and data governance via Unity Catalog, role-based access control (RBAC), and Key Vault integration.
• Collaborate with data scientists, DevSecOps engineers, and cybersecurity SMEs to ensure secure data processing, model deployment and operationalize the deployed models.
• Support model bias detection, adversarial robustness, and interpretability.
• Collaborate on ATO documentation and contribute to ML-specific security artifacts and POA&Ms.

Mandatory Skills & Qualifications

• Must be legally eligible for employment in the U.S.
• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field
• 5 or more years of hands-on experience in data engineering (preferably in cloud environment) with 3 or more years of experience in Machine Learning engineering roles, preferably in secure or classified environments
• Strong programming skills in Python, SQL, PySpark, Jupyter notebooks, and distributed computing
• Experienced in applying or fine-tuning foundational models or large language models (LLM) in mission critical settings.
• Demonstrated experience in designing and integrating diverse data sources to support Agentic AI systems, including structured, unstructured, and real-time data pipelines in production environments
• Proficiency with Retrieval-Augmented Generation (RAG) frameworks, including implementation of vector databases, embedding models, and retrieval mechanisms to enhance contextual relevance and model performance
• Expert level proficiency in –
• Azure Databricks, PySpark, scikit-learn
• MLflow for model tracking and deployment
• Azure ML and associated MLOps tools
• Experienced in deploying ML models securely in cloud-native architectures
• Knowledge of secure data handling, encryption, and role-based access in Azure
• Proficiency in deploying microservices and models APIs using Docker, Cloud Run, or Kubernetes (EKS or GKE)
• Ability to integrate structured, semi-structured, and unstructured data from APIs, RDBMS, and/or streaming sources into Snowflake or Azure SQL DW

Preferred Skills & Qualifications

• Unity Catalog, Delta Live Tables, Databricks Repos
• Deep Learning frameworks like TensorFlow, PyTorch
• CI/CD for AI/ML (MLOps) using Azure DevOps or GitHub Actions
• Familiarity with DoD data strategy, RMF, NIST 800-53, CMMC, and/or FedRAMP
• Experience in handling classified data, synthetic data generation, or mission-critical AI/ML systems
• Experience working in Azure Government, IL5/6, or JWCC environments
• Certification such as:
• DP-100: Designing and Implementing a Data Science Solution on Azure
• AZ-305: Azure Solutions Architect
• DP-700: Fabric Data Engineer Associate