AI/ML Engineer | Talent Marketplace

• 3–5 years of experience in machine learning engineering, data science, or related field.
• Proficiency in Python and core ML libraries (scikit-learn, TensorFlow, PyTorch, or similar).
• Strong understanding of machine learning fundamentals (supervised/unsupervised learning, model evaluation, feature engineering).
• Experience deploying ML models to production environments (APIs, batch pipelines, or embedded systems).
• Familiarity with data manipulation and analysis tools (Pandas, NumPy, SQL).
• Solid software engineering practices including version control, testing, and code quality.
• Strong analytical and problem-solving skills.

Nice to have:

• Experience with LLMs, prompt engineering, and generative AI applications (OpenAI, Anthropic, LangChain, or similar).
• Familiarity with MLOps platforms (MLflow, Weights & Biases, SageMaker, Vertex AI, or similar).
• Experience with cloud ML infrastructure (AWS, GCP, or Azure AI/ML services).
• Knowledge of data engineering tools and pipelines (Spark, Airflow, dbt, or similar).
• Experience with NLP techniques (transformers, embeddings, RAG pipelines).
• Advanced degree (M.S. or Ph.D.) in Computer Science, Statistics, or related field.

• Design, develop, and deploy machine learning models for classification, regression, NLP, computer vision, recommendation, or other use cases.
• Work with data engineers to build and maintain data pipelines for ML model training and inference.
• Evaluate and select appropriate algorithms, frameworks, and architectures.
• Train, validate, and fine-tune models using best practices to avoid overfitting.
• Deploy models to production and build robust inference pipelines.
• Monitor model performance post-deployment and implement retraining and drift detection strategies.
• Collaborate with product and engineering teams to integrate AI features into applications.
• Conduct experiments, document findings, and present insights to stakeholders.
• Stay updated on AI/ML research and assess business applicability.
• Contribute to MLOps practices, tooling, and infrastructure.