AI Systems Architectural Lead

2 days ago

India beBeeLeadership Full time ₹ 15,00,000 - ₹ 25,00,000
Job Description

We are seeking an exceptional AI Systems Architectural Lead to spearhead the design, development, and deployment of advanced machine learning solutions. This leadership role requires a visionary and technically skilled professional who can guide a team of AI/ML engineers, contribute hands-on to critical technical challenges, and collaborate with cross-functional teams to deliver impactful AI solutions.

Technical Leadership:
  • Lead the architecture, development, and deployment of machine learning models and AI systems across a range of use cases.
  • Coach and mentor a team of ML engineers and data scientists.
Project Management:
  • Drive planning, execution, and delivery of AI/ML projects.
  • Architect scalable, secure, and high-performance ML pipelines and services using cloud-native tools and MLOps best practices.
Collaboration:
  • Work closely with product managers, data engineers, and DevOps teams to translate business problems into AI-driven solutions.
Required Skills and Experience:

Bachelor's or Master's in Computer Science, Machine Learning, Data Science, or related technical field is required. The ideal candidate will have 6-10+ years of experience in software engineering or AI/ML, with strong programming skills in Python and experience with ML libraries such as TensorFlow and PyTorch.

The successful candidate will have a deep understanding of ML fundamentals: feature engineering, model evaluation, optimization, and deployment. Proficiency in designing and building data pipelines, real-time processing, and containerization (Docker, Kubernetes) is also essential. Additionally, experience with cloud platforms (AWS, GCP, or Azure) and vector databases is highly desirable.

Additional Requirements:
  • Hands-on experience with LLMs, RAG pipelines, or generative AI applications.
  • Familiarity with agentic AI frameworks (LangChain, CrewAI, AutoGPT).
  • Contributions to open-source AI/ML projects or published research.
  • Knowledge of responsible AI practices, explainability (XAI), and model governance.
  • Strong leadership and team-building skills.
  • Clear and persuasive communication with both technical and non-technical stakeholders.

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