AI/ML Engineer
Job Role: AI/ML Engineer
9+ Years of Experience
Location Type: Onsite
Project type: W2 contract
Key Responsibilities:
Design and build end-to-end machine learning pipelines covering data ingestion, feature engineering, model training, evaluation, and production serving
Develop and fine-tune Large Language Models, transformer architectures, and Generative AI solutions for enterprise use cases
Build and deploy scalable REST and gRPC APIs to serve model predictions at high throughput and low latency
Architect and implement MLOps workflows including experiment tracking, model versioning, CI/CD for ML, drift monitoring, and automated retraining
Develop LLM-powered products using RAG pipelines, prompt engineering, and agentic AI frameworks
Build cloud-native AI infrastructure on AWS, GCP, or Azure using Docker and Kubernetes
Optimize models using quantization, pruning, distillation, and ONNX for production efficiency
Collaborate with data scientists and engineering teams to productionize research prototypes into reliable, maintainable services
Mentor junior engineers, lead code reviews, and drive technical excellence across teams
Required Skills and Qualifications
9+ years of total software engineering experience with majority focused on AI and ML roles
5+ years of hands-on experience designing and deploying ML and AI systems in production environments
Expert-level Python development including async programming, performance profiling, and clean architecture
Deep expertise in PyTorch, TensorFlow, or JAX along with strong working knowledge of scikit-learn, XGBoost, and LightGBM
Hands-on experience with Large Language Models including fine-tuning open-source models, building RAG pipelines, and working with HuggingFace Transformers, LangChain, and LlamaIndex
Proven MLOps experience with MLflow, Kubeflow, Apache Airflow, SageMaker, or Vertex AI
Cloud platform proficiency on AWS, GCP, or Azure with containerized deployments via Docker and Kubernetes
Strong data engineering skills including PySpark, pandas, SQL, and data warehouses like Snowflake, BigQuery, or Redshift
Experience across one or more AI domains such as NLP, Computer Vision, Recommendation Systems, Time-Series Forecasting, or Generative AI
Bachelor's, Master's, or PhD in Computer Science, Engineering, Mathematics, Statistics, or equivalent practical experience.