What are the responsibilities and job description for the AI/ML Engineer (W2, F2F) position at SIRITECH SOLUTIONS CORP?
Position: AI/ML Engineer
Location: NYC NY/Atlanta GA (In-Person Interview)
Duration: 12 Months
Need Only Locals to NY and GA
Face to Face Interview - W2 Contract only
Skills And Responsibilities
Build end-to-end AI/ML systems, including scalable ML pipelines, production-grade models, and full MLOps infrastructure for training, deployment, monitoring, and automation.
Develop solutions using Python, Docker, Kubernetes, Bash, PowerShell, SQL/NoSQL/vector databases, and cloud platforms such as AWS, Azure, GCP, or OCI.
Skilled in Apache Kafka for event-driven systems
Design and deploy ML models across domains like NLP/LLMs (BERT, GPT, RAG, fine-tuning), time-series forecasting, recommender systems, and distributed training on multi-GPU or multi-node setups.
Use MLOps and workflow tools including MLflow, Weights & Biases, Kubeflow, Airflow, and cloud AI services (Azure AI, AWS SageMaker/Bedrock, GCP Vertex AI, OCI AI).
Build internal tools, CLI-first utilities, and efficient ML workflows for high-scale production environments.
Optional experience includes CI/CD (Azure DevOps, GitHub Actions, Jenkins), computer vision (PyTorch/TensorFlow), Go/Rust, feature stores, model optimization, edge deployment, A/B testing, open-source ML, and real-time streaming (Kafka, Kinesis).
Location: NYC NY/Atlanta GA (In-Person Interview)
Duration: 12 Months
Need Only Locals to NY and GA
Face to Face Interview - W2 Contract only
Skills And Responsibilities
Build end-to-end AI/ML systems, including scalable ML pipelines, production-grade models, and full MLOps infrastructure for training, deployment, monitoring, and automation.
Develop solutions using Python, Docker, Kubernetes, Bash, PowerShell, SQL/NoSQL/vector databases, and cloud platforms such as AWS, Azure, GCP, or OCI.
Skilled in Apache Kafka for event-driven systems
Design and deploy ML models across domains like NLP/LLMs (BERT, GPT, RAG, fine-tuning), time-series forecasting, recommender systems, and distributed training on multi-GPU or multi-node setups.
Use MLOps and workflow tools including MLflow, Weights & Biases, Kubeflow, Airflow, and cloud AI services (Azure AI, AWS SageMaker/Bedrock, GCP Vertex AI, OCI AI).
Build internal tools, CLI-first utilities, and efficient ML workflows for high-scale production environments.
Optional experience includes CI/CD (Azure DevOps, GitHub Actions, Jenkins), computer vision (PyTorch/TensorFlow), Go/Rust, feature stores, model optimization, edge deployment, A/B testing, open-source ML, and real-time streaming (Kafka, Kinesis).