Vigyanlabs - AI Engineering Manager

The Role

AI Engineering Manager

Responsibilities

• Team Leadership & Development : Lead, mentor, and inspire a team of AI/ML engineers and data scientists. Conduct regular one-on-ones, performance reviews, and provide continuous feedback to support professional growth.
• Technical Strategy & Vision : Develop and execute the technical strategy for AI initiatives, aligning it with company-wide goals. This includes defining the technology stack, architectural patterns, and best practices for model development, deployment, and monitoring.
• Project & Budget Management : Oversee the entire project lifecycle for AI projects, from ideation to production. Ensure projects adhere to timelines and budgets, and are delivered with high quality.
• Performance Monitoring : Define and implement key performance indicators (KPIs) for the engineering team and AI models, and regularly report on performance to stakeholders.
• Stakeholder Communication : Act as the primary point of contact for the engineering team, effectively communicating project status, goals, and technical challenges to both technical and non-technical stakeholders.
• Cross-Functional Collaboration : Coordinate with product managers, data scientists, and other internal teams to ensure seamless project execution and alignment on goals.
  

Qualifications

Education :

• Bachelor's degree in Computer Science, Engineering, or a related field. A Master's or Ph.D. with a specialization in Machine Learning or Artificial Intelligence is a significant plus.
  

Experience

• 3+ years of experience in a management or lead role within an engineering team.
• Proven hands-on experience in the AI/ML domain, including developing and deploying machine learning models, working with large datasets, and managing data pipelines.
• Experience with cloud platforms (e.g., AWS, GCP, Azure) and their AI/ML services (e.g., Sagemaker, Vertex AI, MLFlow).
  

Technical Skills (AI Focus)

• Strong understanding of core machine learning concepts, including supervised, unsupervised, and reinforcement learning.
• Familiarity with deep learning frameworks such as TensorFlow, PyTorch, or Keras.
• Proficiency in programming languages commonly used in AI/ML, such as Python and R.
• Experience with data processing and big data technologies like Spark, Hadoop, or Dask.
• Knowledge of MLOps principles and tools for model versioning, serving, and monitoring.
  

Core Competencies

• Exceptional interpersonal and communication skills, with the ability to articulate complex technical concepts clearly to diverse audiences.
• Strong leadership and mentoring abilities to motivate and develop a high-performing team.
• Excellent problem-solving skills and a strategic mindset.
• Ability to thrive in a dynamic, fast-paced environment and manage multiple priorities effectively.