Machine Learning-Practice Head

1 month ago

india OptiSol Business Solutions Full time

Experience: 4 to 7 years

Salary Range: Upto 23LPA


“We are a tribe of happy people obsessed with customer success “

Sample our work culture at Life at OptiSol Portal


Mandatory Skills: Machine Learning | Predictive Analytics | CV or NLP | Python | Cloud (AWS or Azure or GCP) | Model Development | Team Handling | Project Handling | Client Interactions


Skills Required:


  • Lead and mentor a team of machine learning engineers, data scientists, and researchers in designing, developing, and deploying machine learning solutions.
  • Define and implement best practices, methodologies, and standards for machine learning model development, evaluation, and deployment.
  • Collaborate with clients and stakeholders to understand business requirements, gather project specifications, and provide accurate estimations for project timelines and resources.
  • Drive innovation and stay updated with the latest advancements in machine learning, artificial intelligence, predictive analytics, computer vision, and natural language processing.
  • Oversee the end-to-end machine learning project lifecycle, from data collection and preprocessing to model training, evaluation, and deployment in production environments.
  • Ensure the scalability, reliability, and performance of machine learning systems, and optimize models for accuracy, speed, and efficiency.
  • Conduct code reviews, quality assurance, and performance tuning of machine learning algorithms, pipelines, and infrastructure.
  • Foster a collaborative and inclusive team culture, encourage knowledge sharing, continuous learning, and professional development among team members.
  • Collaborate with cross-functional teams including product managers, software engineers, data engineers, and domain experts to deliver integrated solutions that meet business goals and user requirements.
  • Provide technical leadership, guidance, and support in resolving complex technical challenges, troubleshooting issues, and mitigating risks.
  • Represent the Machine Learning Engineering practice in client meetings, conferences, and industry events, and contribute to thought leadership initiatives, white papers, and case studies.


Required Technical Skills:


  • Proficiency in machine learning techniques, algorithms, and frameworks such as TensorFlow, PyTorch, Scikit-learn, and Keras.
  • Experience with at least two of the following domains: Predictive Analytics, Computer Vision, Natural Language Processing (NLP).
  • Strong understanding of data science principles, statistical analysis, and experimental design.
  • Knowledge of programming languages such as Python, R, or Scala for machine learning model development, data manipulation, and analysis.
  • Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and distributed computing frameworks (e.g., Apache Spark) for scalable machine learning deployments.


Leadership and Management Skills:


  • Proven experience leading and managing machine learning teams, setting technical direction, and driving team performance.
  • Ability to communicate effectively with clients, gather requirements, and provide accurate estimations for project delivery.
  • Strong project management skills, including planning, prioritization, resource allocation, and risk management.
  • Excellent problem-solving abilities, critical thinking, and decision-making skills in complex technical environments.
  • Experience in fostering a culture of collaboration, innovation, and continuous improvement within the team.


Communication and Collaboration:


  • Excellent communication skills with the ability to convey technical concepts to non-technical stakeholders, present solutions, and negotiate project scope.
  • Collaborative mindset with the ability to work effectively with cross-functional teams, drive consensus, and build strong relationships with clients and partners.
  • Experience in leading client meetings, workshops, and presentations, and managing client expectations throughout the project lifecycle.

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