Staff Machine Learning Engineer

Key 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.

Preferred Qualifications

• 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.

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

• 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