AI/ML/Data Science Engineer

Role & responsibilities

Model Development and Deployment:

Design, build, and deploy machine learning and deep learning models to solve complex business problems.

Optimize model performance and scalability for real-world applications.

Data Processing and Analysis:

Collect, preprocess, and analyze large datasets to extract meaningful insights.

Perform exploratory data analysis (EDA) to identify patterns and trends.

AI/ML Solutions:

Implement AI/ML techniques such as natural language processing (NLP), computer vision, and predictive analytics.

Develop and fine-tune algorithms for classification, regression, clustering, and recommendation systems.

Collaboration and Stakeholder Engagement:

Collaborate with cross-functional teams, including product managers, engineers, and data analysts, to define project requirements and deliver solutions.

Translate business objectives into technical requirements and actionable AI/ML models.

Research and Innovation:

Stay updated with the latest advancements in AI, ML, and data science technologies.

Research and propose new methodologies to improve existing processes or develop new capabilities.

Production Integration:

Work with engineering teams to integrate AI/ML models into production environments.

Monitor and maintain the performance of deployed models.

Preferred candidate profile

Requirements:

Education: Bachelors or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related field.

Experience:

5+ years of hands-on experience in AI, ML, or data science roles.

Proven track record of deploying models in real-world production environments.

Skills Required:

5+ years of experience in data science, machine learning, and deploying production-grade models.

Proficiency in Python/R and frameworks such as TensorFlow, PyTorch, or Scikit-learn.

Expertise in cloud platforms (AWS, Azure, GCP) for model deployment (e.g., SageMaker, Vertex AI, or Azure ML).

Strong experience with containerization tools (Docker) and orchestration platforms (Kubernetes).

Hands-on experience with CI/CD pipelines and MLOps frameworks.

Solid understanding of databases (SQL and NoSQL) and big data technologies like Spark, Hadoop, etc.

Knowledge of software engineering principles, including version control (Git) and testing methodologies.

Other Skills:

Excellent problem-solving and analytical skills.

Strong communication skills to explain technical concepts to non-technical stakeholders.

Ability to work independently and collaboratively in a fast-paced environment.

Perks and benefits