Staff Machine Learning Engineer

Requirements

• Key Responsibilities
• 10+ years of experience in Machine Learning, Data Science, and AI Engineering.
• Proven track record of delivering production-grade AI/ML products at scale.
• Experience leading complex technical initiatives and influencing engineering direction.
• Experience mentoring engineers and driving technical excellence across teams.
• Strong expertise in Python, SQL, and distributed computing frameworks such as Spark.
• PyTorch
• TensorFlow
• Scikit-learn
• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• Agentic AI Systems
• Reinforcement Learning concepts
• AI Evaluation Frameworks
• Docker
• Kubernetes
• AWS, Azure, or GCP
• Vector Databases
• API and Microservices Architecture
• MLOps
• Model Deployment
• Feature Stores
• Experiment Tracking
• Observability and Monitoring
• Leadership Attributes
• Strong architectural and systems-thinking mindset.
• Ability to influence without authority and drive cross-functional alignment.
• Exceptional communication and stakeholder management skills.
• Passion for mentoring, innovation, and continuous learning.
• Must-have skills
• Applied Machine Learning
• Good-to-have skills
• Machine Learning, AI ENGINEERING
• Technical Leadership
• Define and drive the technical vision for Machine Learning and Generative AI initiatives.
• Lead architecture reviews and establish best practices for scalable AI systems.
• Mentor and guide ML engineers and data scientists across teams.
• Influence product strategy through AI-driven innovation and technical thought leadership.
• Partner with Engineering leadership to build scalable, reliable, and secure AI platforms.
• Machine Learning & Data Science
• Design, develop, and deploy large-scale ML solutions in production environments.
• Build advanced predictive models, recommendation systems, forecasting solutions, NLP applications, and deep learning systems.
• Problem definition
• Data acquisition and exploration
• Feature engineering
• Model development
• Model evaluation and validation
• Production deployment
• Monitoring, governance, and continuous improvement
• Develop frameworks and reusable components to accelerate ML development across teams.
• Establish model governance, explainability, fairness, and compliance standards.
• Generative AI & LLM Applications
• OpenAI
• Azure OpenAI
• Anthropic Claude
• Llama
• Mistral
• Gemini
• Advanced RAG architectures
• Multi-agent workflows
• AI orchestration frameworks
• Prompt engineering and evaluation frameworks
• Fine-tuning and model adaptation pipelines
• Knowledge graph-assisted AI systems
• AI observability and evaluation frameworks
• Lead experimentation and adoption of emerging AI technologies to create competitive advantage.
• Platform Engineering & MLOps
• Architect scalable ML platforms and infrastructure.
• Build and optimize end-to-end ML pipelines.
• CI/CD for ML
• Model serving
• Monitoring and observability
• Automated retraining pipelines
• Model governance and security
• Optimize system performance, scalability, reliability, and cost efficiency.
• Cross-Functional Collaboration
• Partner with Product Managers, Engineering leaders, and Business stakeholders to identify high-impact AI opportunities.
• Translate business problems into scalable AI solutions.
• Define success metrics and measure business impact.
• Drive AI adoption and technical excellence across the organization.

Technical Leadership

Machine Learning & Data Science

Generative AI & LLM Applications

Platform Engineering & MLOps

Cross-Functional Collaboration

Technical Skills

• Strong expertise in Python, SQL, and distributed computing frameworks such as Spark.
• Deep knowledge of machine learning and deep learning frameworks:
• PyTorch
• TensorFlow
• Scikit-learn
• Strong expertise in:
• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• Agentic AI Systems
• Reinforcement Learning concepts
• AI Evaluation Frameworks
• Hands-on experience with:
• Docker
• Kubernetes
• AWS, Azure, or GCP
• Vector Databases
• API and Microservices Architecture
• Expertise in:
• MLOps
• Model Deployment
• Feature Stores
• Experiment Tracking
• Observability and Monitoring

Leadership Attributes

Responsibilities

• Define and drive the technical vision for Machine Learning and Generative AI initiatives.
• Lead architecture reviews and establish best practices for scalable AI systems.
• Mentor and guide ML engineers and data scientists across teams.
• Influence product strategy through AI-driven innovation and technical thought leadership.
• Partner with Engineering leadership to build scalable, reliable, and secure AI platforms.
• Design, develop, and deploy large-scale ML solutions in production environments.
• Build advanced predictive models, recommendation systems, forecasting solutions, NLP applications, and deep learning systems.
• Drive the complete machine learning lifecycle:
• Problem definition
• Data acquisition and exploration
• Feature engineering
• Model development
• Model evaluation and validation
• Production deployment
• Monitoring, governance, and continuous improvement
• Develop frameworks and reusable components to accelerate ML development across teams.
• Establish model governance, explainability, fairness, and compliance standards.
• Architect and deliver enterprise-scale GenAI solutions leveraging:
• OpenAI
• Azure OpenAI
• Anthropic Claude
• Llama
• Mistral
• Gemini
• Design and implement:
• Advanced RAG architectures
• Agentic AI systems
• Multi-agent workflows
• AI orchestration frameworks
• Prompt engineering and evaluation frameworks
• Fine-tuning and model adaptation pipelines
• Knowledge graph-assisted AI systems
• AI observability and evaluation frameworks
• Lead experimentation and adoption of emerging AI technologies to create competitive advantage.
• Architect scalable ML platforms and infrastructure.
• Build and optimize end-to-end ML pipelines.
• Drive MLOps best practices including:
• CI/CD for ML
• Model serving
• Feature stores
• Experiment tracking
• Monitoring and observability
• Automated retraining pipelines
• Model governance and security
• Optimize system performance, scalability, reliability, and cost efficiency.
• Partner with Product Managers, Engineering leaders, and Business stakeholders to identify high-impact AI opportunities.
• Translate business problems into scalable AI solutions.
• Define success metrics and measure business impact.
• Drive AI adoption and technical excellence across the organization.