Generative AI

Job Summary:

We are seeking a highly skilled and visionary Senior Tech Lead to spearhead our initiatives in Artificial Intelligence (AI) and Generative AI (GenAI). This role demands deep technical expertise, strategic thinking, and leadership capabilities to drive innovation and deliver cutting-edge AI solutions across products and platforms.

Key Responsibilities:

• Technical Leadership:

Lead the design, development, and deployment of AI and GenAI models and systems.

Architect scalable and robust AI solutions using modern frameworks and cloud platforms.

Guide the team in applying machine learning, deep learning, NLP, and GenAI techniques.

• Project Management:

Collaborate with cross-functional teams to define project scope, timelines, and deliverables.

Ensure timely delivery of high-quality AI solutions aligned with business goals.

• Innovation & Strategy:

Stay ahead of emerging trends in AI and GenAI and evaluate their potential impact.

Drive innovation by identifying opportunities to integrate GenAI into existing and new products.

• Mentorship & Collaboration:

Mentor junior engineers and data scientists.

Foster a culture of experimentation, learning, and collaboration.

• Governance & Ethics:

Ensure responsible AI practices, including fairness, transparency, and privacy.

Implement model monitoring, validation, and continuous improvement processes.

Required Qualifications:

• Bachelors or master’s degree in computer science, Data Science, AI, or related field.
• 8+ years of experience in software engineering, with 4+ years in AI/ML.
• Proven experience with GenAI technologies (e.g., LLMs, diffusion models, transformers).
• Strong programming skills in Python and familiarity with frameworks like TensorFlow, PyTorch, Hugging Face, LangChain, etc.
• Experience with cloud platforms (AWS, Azure, GCP) and MLOps tools.
• Excellent problem-solving, communication, and leadership skills.

Preferred Qualifications:

• PhD in AI/ML or related field.
• Experience with deploying GenAI models in production.
• Knowledge of data governance, model interpretability, and ethical AI practices.
• Contributions to open-source AI projects or publications in AI conferences.