AI Lead

AI Lead

Key Responsibilities:

• Lead and mentor a team of 4–5 AI/ML engineers, guiding them through the full project lifecycle from research to production deployment.
• Design and own both

  high-level and low-level architecture

  for AI and GenAI-based applications, ensuring scalability, performance, and maintainability.
• Collaborate with sales and business development teams to create

  technical proposals

  , including

  solution design

  ,

  architecture diagrams

  , and

  high-level estimates

  for Sales Qualified Leads (SQLs).
• Work as an

  individual contributor

  when needed, developing key components of the solution to accelerate delivery and set best practices.
• Research, design, and implement machine learning and deep learning models, including

  fine-tuning and deploying LLMs

  (e.g., GPT, BERT) for production use cases.
• Build

  NLP pipelines

  and

  retrieval-augmented generation (RAG)

  systems using tools like LangChain, LangGraph, and vector databases (e.g., FAISS, Pinecone, Weaviate).
• Apply statistical techniques to feature engineering, model evaluation, and performance optimization.
• Ensure adherence to code quality, testing, CI/CD, and documentation standards.
• Stay current with AI/ML advancements and proactively propose innovative solutions.
• Conduct code reviews, encourage peer learning, and contribute to a strong engineering culture.

Required skills and qualifications:

• Bachelor’s, Master’s, or Ph.D. in Computer Science, Engineering, Mathematics, or a related field.
• 7+ years of hands-on experience in AI/ML model development and deployment.
• Proven experience in leading technical teams and delivering production-grade AI/ML solutions.
• Strong architectural skills with experience designing

  end-to-end solutions

  involving cloud, APIs, and ML models.
• Demonstrated experience supporting

  technical pre-sales efforts

  through proposals, estimates, and client presentations.
• Proficient in Python and ML libraries such as TensorFlow, PyTorch, scikit-learn.
• Hands-on experience with NLP tools like HuggingFace Transformers, SpaCy, and NLTK.
• Practical knowledge of

  fine-tuning and deploying LLMs

  , and building GenAI solutions.
• Familiarity with LangChain, LangGraph, and vector stores (e.g., FAISS, Pinecone).
• Strong understanding of classical ML algorithms (SVM, Decision Trees, etc.) and when to use them.
• Experience deploying models and services using REST APIs, Docker, and CI/CD pipelines.
• Exposure to cloud platforms such as AWS, GCP, or Azure.
• Excellent analytical thinking, problem-solving, and communication skills.