Description

Python (latest stable version, e.g., Python 3.8+) — extensive hands-on experience supporting training, fine-tuning, and inference of large AI models (supporting 5–10 years)

AI Frameworks: PyTorch, TensorFlow — proven expertise in training, deploying, and optimizing deep learning models supporting generative and multimodal capabilities

Large Language Models: GPT, Claude, Llama, Gemini, or similar — experienced in prompt engineering, fine-tuning, and deployment support (supporting 3+ years)

Cloud Platforms: AWS, Azure, or GCP — experience deploying and managing scalable AI models supporting enterprise solutions (preferred support, 3+ years)

Model orchestration & management: MLflow, Kubeflow supporting model lifecycle, versioning, and monitoring (preferred support)

Data processing: Pandas, NumPy supporting data preparation and feature engineering support

Preferred Software Skills:

AI model evaluation and bias mitigation tools supporting model fairness and performance assessment

MLOps pipelines supporting continuous deployment, retraining, and automation support (Kubeflow, TFX, or similar)

Multi-modal processing frameworks supporting text, images, and audio inputs (preferred)

Overall Responsibilities

Lead the design, training, and deployment of large language models and multimodal agents supporting enterprise automation and insights

Develop scalable AI pipelines supporting real-time inference, retraining, and model monitoring in cloud environments

Collaborate with data scientists, platform engineers, and business stakeholders to translate use cases into operational AI systems supporting automation and decision support

Support prompt engineering, model evaluation, bias detection, and performance tuning for operational reliability and fairness

Automate deployment, versioning, and monitoring workflows supporting MLOps and responsible AI standards

Conduct model validation, interpretability checks, and security assessments supporting compliance in regulated environments

Support enterprise data pipelines supporting multimodal, retrieval-augmented, and knowledge-based AI systems supporting operational transparency

Document model architecture, training, tuning, deployment procedures, and operational metrics supporting audit and compliance regimes

Technical Skills (By Category)

Languages & Frameworks (Essential):

Python supporting large-scale model training, fine-tuning, and scripting for automation

PyTorch and TensorFlow supporting deep learning model development and deployment

Supporting libraries: Hugging Face Transformers, LangChain, support for RAG architecture and plugin integration

Data & Model Management:

Pandas, NumPy supporting data preparation, feature engineering, and validation

Model versioning tools: MLflow, Kubeflow supporting lifecycle management and deployment support

Cloud & Infrastructure:

AWS, Azure, or GCP supporting scalable deployment and inference in enterprise settings (preferred)

Container orchestration support: Docker, Kubernetes supporting scalable, cloud-native AI systems

Model Evaluation & Monitoring:

Tools supporting bias detection, fairness assessment, and inference monitoring (e.g., TensorBoard, custom dashboards)

Experience Requirements

4+ years supporting enterprise AI/ML projects, including large language models, retrieval-augmented generation, and multimodal systems

Proven experience in deploying AI models supporting automation, knowledge management, and operational workflows

Extensive hands-on expertise in cloud AI deployment, orchestrating model lifecycle, and scalable inference support (preferably in regulated environments)

Experience supporting responsible AI practices, model fairness, and security in enterprise settings

Day-to-Day Activities

Develop, fine-tune, and deploy large language models and multimodal agents supporting enterprise automation workflows

Build and automate AI pipelines supporting training, inference, retraining, and model monitoring workflows in a cloud environment

Collaborate closely with data scientists, platform teams, and business units to deliver scalable AI solutions supporting operational efficiency

Conduct bias, fairness, and security evaluations supporting compliance and trustworthy AI practices

Troubleshoot and optimize model inference latency, retraining workflows, and deployment environments supporting enterprise scale

Automate model deployment, monitoring, and retraining pipelines supporting continuous delivery and performance tuning

Document AI architecture, models, training, and operational procedures supporting audit readiness and governance

Qualifications

Bachelor’s or Master’s degree in Data Science, Computer Science, AI, or related technical fields

4+ years supporting enterprise AI/ML solutions, including large language models, retrieval-augmented systems, and multimodal agents

Certifications supporting cloud platforms (AWS, GCP, Azure) or responsible AI practices are advantageous (preferred)

Proven experience supporting or leading compliant, scalable AI systems supporting data privacy and fairness standards

Key Skills

Education

Bachelor's or Master's degrees