Staff Software Machine Learning Engineer

Apply leading Models to solve business problems

• Use models from GPT-* to LLama-* to solve wide range of business problems from sales tech, employee productivity, coding and automation
• Build RAG frameworks
• Build Langchain modules

Model Development and Implementation:

• Design, develop, and deploy machine learning models to solve complex problems and improve user experiences, operational efficiency, or system performance.
• Utilize a variety of data sources, types, and structures to extract actionable insights through predictive analytics and data mining techniques.

Research and Innovation:

• Stay abreast of the latest developments in the field of machine learning and artificial intelligence. Evaluate emerging trends and technologies for potential adoption to maintain and expand competitive advantage.
• Lead research initiatives that test new algorithms, evaluate new methodologies, and explore innovative uses of data that can lead to scalable solutions.

Team Leadership and Development:

• Lead and mentor a team of machine learning engineers and data scientists. Ensure the continuous professional growth of team members through clear goal-setting, regular feedback, and development opportunities.
• Foster a collaborative and inclusive team environment that encourages innovation and iterative learning.

Cross-Functional Collaboration:

• Work closely with product management, software engineering, and data engineering teams to integrate machine learning models into larger software systems and product offerings.
• Partner with stakeholders across the organization to understand business needs and translate them into technical requirements and actionable machine learning projects.

Project Management:

• Oversee the full project lifecycle for multiple machine learning initiatives, from ideation and data collection to model development and deployment.
• Manage resources, timelines, and risks effectively, ensuring that projects meet their objectives and are delivered on schedule.
• Performance Monitoring and Model Optimization:
• Continuously monitor the performance of deployed models, identifying opportunities for improvement and optimization.
• Implement robust testing and validation strategies to ensure model accuracy and reliability over time.

• Experience in applying Gen-AI/LLM in production
• Managed at least a team 4+ engineers for end-to-end deliverables, for more than 2 years
• Education: Bachelors degree in Computer Science, Engineering, or a closely related field. Advanced degrees (masters or PhD) in fields that emphasize software engineering or machine learning are preferred.
• Professional Experience: At least 8 years of experience in software development with a proven track record in both software engineering and research or applied machine learning projects.

Technical Expertise:

• Expertise in programming using languages such as Python, Java, C++, or Scala.
• Proficient with modern software engineering tools and methodologies (eg, version control, CI/CD, agile development practices).
• Extensive experience with machine learning libraries and frameworks like TensorFlow, PyTorch, or Keras.

Data Proficiency:

• Strong capabilities in handling large datasets, with skills in SQL, NoSQL databases, data modeling, and ETL processes.
• Experience designing and implementing systems that collect, manage, and convert raw data into actionable insights through machine learning models.

Machine Learning Application Experience:

• Solid foundation in applying machine learning algorithms to real-world problems, optimizing algorithms for scalability and performance.
• Hands-on experience in building, scaling, and maintaining production-level machine learning models.