Engineering Manager-AI/ML

Role & responsibilities

• Lead and manage a team of Data Scientists and ML Engineers in designing, developing, and deploying AI/ML and Generative AI applications.
  
• Drive the end-to-end lifecycle of ML solutions including data preprocessing, model development, evaluation, deployment, and monitoring in production.
  
• Design and implement scalable, secure, and high-performing AI/ML architectures and pipelines on cloud platforms such as GCP, AWS, or Azure.
  
• Develop and fine-tune Large Language Models (LLMs) and build RAG (Retrieval-Augmented Generation) based GenAI solutions for business use cases.
  
• Perform prompt engineering and optimization to improve LLM accuracy, personalization, and contextual understanding.
  
• Collaborate with product managers, architects, and cross-functional teams to define AI/ML strategies and integrate models into enterprise applications.
  
• Build and maintain data pipelines involving data ingestion, transformation, and feature engineering for model training and evaluation.
  
• Provide technical mentorship, code reviews, and career guidance to team members to build strong ML and data engineering capabilities.
  
• Ensure the reliability, scalability, and cost-effectiveness of AI/ML solutions using best practices in software design and DevOps.
  
• Stay updated with the latest research and advancements in AI, ML, NLP, Vision AI, and Generative AI technologies to drive innovation.
  
• Oversee project planning, estimation, and timely delivery of AI/ML initiatives with high quality and business alignment.
  
• Collaborate with customers to understand requirements, design proof of concepts (POCs), and deliver end-to-end production-ready AI/ML solutions.

Preferred candidate profile

• Experience:

  8 to 12 years of total IT experience, with at least

  5+ years in AI/ML solution design and deployment

  .

• Leadership:

  Minimum

  2 years of experience leading ML or Data Science teams

  (4+ members).

• Technical Expertise:

  Strong hands-on experience in

  Machine Learning, Deep Learning, NLP, Vision AI, and Generative AI (RAG, LLM fine-tuning, Prompt Engineering)

  .

• Cloud Knowledge:

  Practical exposure to

  Google Cloud Platform (GCP)

  or equivalent (AWS / Azure) for building and deploying ML pipelines.

• Programming Skills:

  Proficiency in

  Python, Pandas, NumPy, TensorFlow, PyTorch, Scikit-learn, LangChain, and XGBoost

  .

• Architecture Skills:

  Proven ability to design and implement

  secure, scalable, and high-performance AI/ML architectures

  .

• Educational Qualification:

  Bachelors or Master’s degree in

  Computer Science, Data Science, AI/ML, or related field

  .

• Certifications (Good to Have):

  Google Cloud ML Engineer / TensorFlow Developer Certification.

• Soft Skills:

  Excellent communication, analytical thinking, stakeholder management, and team collaboration skills.