Senior Engineer-AI/ML

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: We are seeking a highly skilled and motivated Senior AI/ML Engineer with 5–7 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 ML pipelines ensuring full compliance with industry standard security and risk framework like RMF / NIST / CMMC frameworks. The ideal candidate should have proficiency in Amazon Web Service (AWS) and/or Google Cloud Platform (GCP) with a solid foundation in data engineering, Machine Learning and MLOps cloud-native tools, and data governance. The ideal candidate should be a team player, responsible for the development and orchestration of AI/ML components of various solutions delivered by Data & A/I Practice for our clients. Essential Functions • Actively involves in requirement gathering workshops from customers, translating the functional requirements into technical solutions, and translating complex technical concepts into actionable insights for stakeholders • Actively participates in architectural discussions independently or under guidance / supervision from Practice Architect and/or Lead Engineer to design and develop effective, efficient, reliable, secure, and scalable data engineering solutions as per the overall data management strategy • Builds end-to-end machine learning pipelines using AWS (e.g., SageMaker, Lambda, S3) or GCP (e.g., Vertex AI, Cloud Functions, BigQuery) for training, evaluation, and model lifecycle management and ensure scalability, reliability, and performance of ML models in production environments • Build, train, and fine-tune models using frameworks like TensorFlow, PyTorch, or Scikit-learn and apply techniques such as hyperparameter tuning, feature engineering, and model evaluation to continuously improve accuracy and efficiency. • Design and implement robust data ingestion, transformation, and storage solutions using cloud-native tools (e.g., AWS Glue, GCP Dataflow) while ensuring data quality, governance, and compliance following industry and/or organizational standards. • Develop and maintain CI/CD pipelines for ML workflows using tools like AWS CodePipeline or GCP Cloud Build automating model deployment, monitoring, and rollback strategies to support continuous delivery. • Implement IAM roles, VPC configurations, and encryption protocols to safeguard data and models following best practices for cost optimization and cloud security. • Collaborate with data scientists, DevSecOps engineers, and cybersecurity SMEs to ensure secure data processing, model deployment and operationalize the deployed models. • Create prototypes and evaluate emerging tools and methodologies to drive innovation within the team. • Occasional support to Sales and Pre-Sales partners to convert opportunity to revenue through thought leadership in the designated area of expertise (AI/ML) Mandatory Skills/Competencies • Bachelor or master degree in Computer Science, Data Science, Engineering, or related field. • 5–7 years of hands-on experience in data engineering (preferably in cloud environment) with 3+ years of experience in Machine Learning engineering roles, preferably in secure or classified environments • Strong proficiency in Python, PySpark, SQL, Jupyter notebooks, and distributed computing and optionally R, Java, or Scala • Strong understanding of core machine learning, deep learning, and NLP • Deep understanding of cloud-native ML services like Amazon SageMaker, AWS Lambda, GCP Vertex AI, and BigQuery ML. • Proficiency in supervised, unsupervised, and deep learning techniques. • Hands-on experience with TensorFlow, PyTorch, Scikit-learn, or similar libraries • Knowledge of CI/CD pipelines, model versioning, and automated deployment and experience with tools like Kubeflow, MLflow, Docker, and Kubernetes. • Production level experience in dealing with structured, semi-structured and unstructured data from APIs, RDBMS, and/or streaming sources