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Software Engineer

Skills

ab testingagileaiapiawsazurecloudcomputer visiondockerfastapifeature engineeringflaskgcpgenerative aigitjupyterkeraskubernetesllmmachine learningmicroservicesmlflowmlopsmodel evaluation

Description

AI/ML Engineer — Job Description

Contract type: W2

About the Role

Soft Standard Solutions is a leading staffing and consulting firm, specializing in connecting skilled IT professionals with top clients across the US and helping candidates secure strong, long-term placements. We are currently hiring a Software Engineer with good experience in Python development and Machine Learning for one of our esteemed clients. This is a great opportunity for someone who enjoys building scalable software while applying ML techniques to solve real-world problems, with the added support of our placement and career guidance services throughout the process.

Roles & Responsibilities

  • Design, develop, and deploy machine learning models for real-world business use cases
  • Perform data collection, preprocessing, feature engineering, and exploratory data analysis
  • Train, evaluate, and fine-tune ML/DL models for accuracy, performance, and scalability
  • Build and deploy AI/ML pipelines into production environments
  • Work with NLP, Computer Vision, or Generative AI/LLM use cases as applicable
  • Integrate ML models into applications via APIs and microservices
  • Collaborate with data engineers, software developers, and product teams to deliver AI-driven solutions
  • Monitor deployed models for performance, drift, and retraining needs (MLOps)
  • Conduct experiments, A/B testing, and document model performance and findings
  • Stay current with emerging AI/ML trends, tools, and research

Required Skillset

  • Core: Python, Statistics, Data Structures & Algorithms
  • ML/DL Frameworks: TensorFlow, PyTorch, Scikit-learn, Keras
  • Data Handling: Pandas, NumPy, SQL, Data Preprocessing, Feature Engineering
  • Specializations: NLP, Computer Vision, Generative AI/LLMs (as applicable)
  • MLOps & Deployment: MLflow, Docker, Kubernetes, Model Serving (Flask/FastAPI)
  • Cloud Platforms: AWS/Azure/GCP (SageMaker, Azure ML, Vertex AI)
  • Other: Git, Jupyter Notebooks, Agile/Scrum, Model Evaluation Metrics

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