Data Scientist - Gen AI

Data Scientist - Gen AI

What You Will Do: Key Responsibilities

• Architect and design AI and NLP solutions

  to address complex business challenges and support strategic decision-making.

• Lead the design and development of scalable machine learning models and applications

  using

  Python, Spark, NoSQL databases

  , and other advanced technologies.

• Spearhead the integration of Generative AI techniques in production systems

  to deliver innovative solutions such as chatbots, automated document generation, and workflow optimization.

• Guide teams in conducting comprehensive data analysis and exploration

  to extract actionable insights from large datasets, ensuring these findings are communicated effectively to stakeholders.

• Collaborate with cross-functional teams

  , including software engineers and data engineers, to integrate AI models into production environments, ensuring scalability, reliability, and performance.

• Stay at the forefront of advancements in AI, NLP, and Generative AI

  , incorporating emerging methodologies into existing models and developing new algorithms to solve complex challenges.
• Provide

  thought leadership on best practices

  for AI model architecture, deployment, and continuous optimization.
• Ensure that

  AI solutions are built with scalability, reliability, and compliance

  in mind.

The Skills You Bring: Required Qualifications

• Experience:

  Minimum of experience in AI, machine learning, or a similar role, with a proven track record of delivering AI-driven solutions (specific years of experience to be determined by the hiring team, but a strong portfolio is key).

• GenAI Implementation:

  Hands-on experience in designing and implementing

  end-to-end GenAI-based solutions

  , particularly in chatbots, document generation, workflow automation, and other generative use cases.

• Programming & Frameworks:

  Expertise in

  Python programming

  and extensive experience with AI frameworks and libraries such as

  TensorFlow, PyTorch, scikit-learn, and vector databases

  .

• Distributed Data Processing:

  Deep understanding and experience with

  distributed data processing using Spark

  .

• Production ML/AI:

  Proven experience in

  architecting, deploying, and optimizing machine learning models in production environments at scale

  .

• Open-Source GenAI Models:

  Expertise in working with

  open-source Generative AI models

  (e.g., GPT-4, Mistral, Code-Llama, StarCoder) and applying them to real-world use cases.

• Cloud-Native Architecture:

  Expertise in

  designing cloud-native architectures and microservices for AI/ML applications

  .