AI/ML Engineer

Position: AI/ML Engineer Contract: W2 Only

Responsibilities

• Design, develop, and deploy machine learning models and AI solutions for enterprise-scale applications across structured and unstructured data.
• Collaborate with cross-functional teams to gather business requirements, perform data exploration, and define model objectives and success metrics.
• Build and optimize end-to-end ML pipelines, including data ingestion, preprocessing, model training, evaluation, and deployment.
• Perform advanced data analysis and feature engineering on large datasets using Python, SQL, and distributed computing tools.
• Integrate ML solutions into production environments via REST APIs, cloud services, or batch processing frameworks.
• Conduct model validation, A/B testing, and performance monitoring to ensure accuracy, fairness, and reliability in production.
• Use tools like MLflow, Airflow, Git, and Docker for experiment tracking, workflow orchestration, and version control.
• Work in Agile or hybrid Agile/Waterfall teams, contributing to planning, retrospectives, and continuous delivery practices.
• Troubleshoot model drift, data quality issues, and production model failures with a focus on root cause analysis and resilience.
• Collaborate on AI/ML modernization initiatives using MLOps, cloud-native technologies, and scalable model serving infrastructure.

Required Skills

• 10 years of experience in machine learning engineering or applied data science.
• Strong proficiency in Python, SQL, and libraries such as scikit-learn, pandas, NumPy, and TensorFlow or PyTorch.
• Experience with designing, training, and deploying supervised and unsupervised learning models.
• Proficient in data processing tools and platforms (e.g., Spark, Databricks, or AWS Glue).
• Solid understanding of ML model lifecycle, software engineering best practices, and CI/CD for ML.
• Experience deploying ML models via REST APIs, containers, or serverless cloud functions.
• Familiar with cloud platforms like AWS, Azure, or Google Cloud Platform, and services like S3, SageMaker, or Vertex AI.
• Experience with Git-based workflows, containerization (Docker), and orchestration tools (Airflow/Kubeflow).

Nice-to-Have

• Hands-on experience with deep learning, NLP, or generative AI frameworks (e.g., Hugging Face Transformers, LangChain).
• Exposure to vector databases (Pinecone, FAISS) or retrieval-augmented generation (RAG) pipelines.
• Familiarity with Kubernetes, Terraform, or CI/CD tools like Jenkins, GitHub Actions.
• Knowledge of responsible AI principles, model fairness, and data governance.
• Understanding of real-time inference systems using Kafka, Redis, or similar platforms.

Soft Skills

• Strong analytical and problem-solving skills backed by hands-on experience in delivering production-grade ML solutions.
• Excellent communication skills to translate complex technical concepts into business value for diverse stakeholders.
• Proven ability to work independently and collaboratively in fast-paced, evolving environments.
• Passion for continuous learning, open-source contributions, and staying up to date with AI/ML advancements.
• Detail-oriented mindset with a strong commitment to data integrity, security, and ethical AI practices.