Lead AI Engineer - Machine Learning

SUMMARY:
Are you a Lead AI Engineer, ready to bridge the gap between business goals and AI technologies, with a focus on cutting-edge solutions, scalability, and ethical compliance? Apply now!

POSITION INFO:
Key Responsibilities:

• Machine Learning Solution Development:
  
  • Model Identification and Design: Identify business processes that can benefit from ML/AI automation and optimization.
  • Model Building and Optimization: Develop and fine-tune custom ML models, including large language models (LLMs), computer vision models, and generative AI solutions.
  • Feature Engineering: Pre-process data, extract features, and scale inputs to enhance model performance.
• Integration and Deployment:
  
  • Integrate ML models into development, pre-production, and production environments.
  • Deploy models using cloud platforms like AWS SageMaker, Azure, or containerized solutions.
  • Monitor and refine model performance in real-world applications to ensure efficiency and accuracy.
• Automation and Workflow Enhancement:
  
  • Develop intelligent ML pipelines to automate manual tasks and streamline workflows.
  • Deliver actionable insights through ML models aligned with business key performance indicators (KPIs).
• Collaboration and Leadership:
  
  • Lead cross-functional teams to execute AI projects effectively.
  • Work closely with technical leads, engineers, and business stakeholders to define project goals and requirements.
  • Communicate technical solutions and project outcomes to non-technical stakeholders.
• Compliance and Ethics:
  
  • Uphold data privacy standards and ethical AI practices, particularly when handling sensitive data.
• Technical Innovation:
  
  • Stay informed about emerging AI technologies (e.g., LLMs, conversational AI, computer vision).
  • Experiment and innovate to tackle complex business challenges using cutting-edge AI solutions.

• Machine Learning and AI: Proficiency in supervised/unsupervised learning, deep learning, NLP, computer vision, LLMs, and ensemble techniques.
• Programming and Tools: Expertise in Python, R, Scikit-Learn, TensorFlow, PyTorch, Docker, and cloud platforms (AWS, Azure, GCP).
• DevOps and Deployment: Experience with CI/CD pipelines, containerization (Docker/Kubernetes), and frameworks like Flask or FastAPI.
• Data Analysis and Visualization: Skilled in tools such as Matplotlib, Seaborn, Tableau, and Big Data frameworks like Spark and Hadoop.
• Collaboration and Communication: Ability to explain complex technical concepts to non-technical stakeholders effectively.

• Leadership: Proven ability to lead and mentor teams, ensuring timely project delivery.
• Problem-Solving: Innovative thinker with a track record of addressing complex business and technical challenges.
• Adaptability: Success in dynamic, remote working environments.
• Business Acumen: Strong focus on aligning technical deliverables with business objectives to maximize ROI.