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AI/ML Architect
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- Posted 2 months ago
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Job Description
Experience: 8–15 Years
Location: Remote / On-site / Hybrid
Employment Type: Full-time / Contract (as applicable)
Job Description
We are seeking an experienced AI/ML Architect to design, build, and scale end-to-end artificial intelligence and machine learning solutions. The ideal candidate will define AI architecture, guide technical teams, and ensure AI systems are robust, scalable, and aligned with business goals.
Key Responsibilities
Location: Remote / On-site / Hybrid
Employment Type: Full-time / Contract (as applicable)
Job Description
We are seeking an experienced AI/ML Architect to design, build, and scale end-to-end artificial intelligence and machine learning solutions. The ideal candidate will define AI architecture, guide technical teams, and ensure AI systems are robust, scalable, and aligned with business goals.
Key Responsibilities
- Design and architect end-to-end AI/ML solutions from data ingestion to deployment
- Define AI/ML technology stack, frameworks, and best practices
- Lead model development, training, validation, and deployment strategies
- Collaborate with data scientists, engineers, product managers, and stakeholders
- Design scalable ML pipelines (MLOps) including CI/CD, monitoring, and retraining
- Ensure data quality, model performance, security, and compliance
- Evaluate and implement cloud-based AI solutions (AWS, Azure, GCP)
- Stay updated with latest AI/ML trends, tools, and research
- Provide technical leadership, mentorship, and architectural guidance
- Strong experience in AI/ML system design and architecture
- Proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn)
- Hands-on experience with deep learning, NLP, computer vision, or generative AI
- Experience with MLOps tools (MLflow, Kubeflow, Airflow, Docker, Kubernetes)
- Strong understanding of data engineering, pipelines, and large-scale systems
- Experience with cloud platforms (AWS SageMaker, Azure ML, or GCP AI)
- Knowledge of APIs, microservices, and scalable system design
- Excellent problem-solving and communication skills
- Experience with LLMs, GenAI, and prompt engineering
- Knowledge of data governance, model explainability, and AI ethics
- Prior experience in leading AI teams or large-scale AI projects
- Master's or PhD in Computer Science, AI, ML, or related field
- Opportunity to lead cutting-edge AI initiatives
- Work with advanced AI/ML technologies and real-world data
- Competitive compensation and benefits
- High-impact role with business and technical ownership
- Collaborative and innovation-driven work environment
More Info
Key Skills
MLflow
Kubeflow
MLOps tools
data engineering pipelines
generative AI
Scikit-learn
GCP AI
cloud platforms
large-scale systems
SageMaker
scalable system design
ML frameworks
AI ML system design and architecture
About Company
Skills:
data engineering , Machine Learning, Deep Learning, Big Data, Cloud Computing, Python Programming, Api Development, Data Visualization, AI ML, Model Deployment, DevOps Practices
Skills:
Pytorch, Gcp, Docker, Azure, Kubernetes, AWS, TensorRT-LLM, PEFT, SFT, Hugging Face Transformers, Accelerate, MLflow, SGLang, TRL, vLLM, TGI, LoRA, QLoRA
Skills:
Mobile Application Development, APEX, Ml, Flows, LLM-based systems, agentic tool-use patterns, agentic systems architecture, automation platforms, Agentforce, platform scalability principles, Lightning Web Components, responsible AI, LLM orchestration, integration patterns, Salesforce platform architecture, cloud-native architecture, Ai, AI risk frameworks, prompt engineering, Salesforce Field Service solution development, Agentforce custom agent frameworks, AI agents, AI governance, Gemini
Skills:
Flows, APEX, Mobile Application Development, Salesforce platform architecture, Lightning Web Components, LLM-based systems, Integration patterns, Prompt engineering, Automation platforms, Agentic systems architecture, AI risk frameworks, AI coding assistants, Salesforce Field Service solution development, Agentforce, Platform scalability principles, Agentic tool-use patterns, LLM orchestration, AI governance, Responsible AI, Cloud-native architecture, Custom agent frameworks