TEKsystems Global Services (TGS) is seeking a Senior AI/ML Engineer to design, develop, and deploy secure, scalable, and high-performance ML pipelines, ensuring full compliance with industry standard security and risk frameworks like RMF / NIST / CMMC frameworks.
Requirements
- 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 into data lakes or storages [e.g., Snowflake, S3, Google Cloud Storage (GCS), etc.]
Responsibilities
- Actively involved in requirement gathering workshops from customers, translating the functional requirements into technical solutions, and translating complex technical concepts into actionable insights for stakeholders.
- Actively participate 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.
- Build 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.
Other
- 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
- Excellent verbal and written communication skills
- Ability to work cross-functionally with product managers, data scientists, and engineering teams
- Up to 50% travel to client sites as per project need
