Senior Staff Engineer, AI

2 weeks ago

Bengaluru, Karnataka, India Movius Interactive Full time
Senior Staff Engineer, AI
Location: Bengaluru, KA, 560076
Key Skills:
  • Architect scalable AI systems
  • Guide model development and deployment best practices
  • Mentor teams across applied ML, data, and backend engineering
Key Responsibilities:
AI System Architecture & Design:
  • Lead the design of large-scale AI/ML systems — from data ingestion to model training, deployment, and monitoring.
  • Define best practices for model lifecycle management, feature engineering, and MLOps infrastructure.
Technical Expertise:
  • Drive high-impact AI initiatives that span multiple teams and product lines.
  • Review and influence the design and implementation of models, APIs, and distributed ML pipelines.
  • Ensure scalability, performance, and ethical use of AI systems.
Research & Innovation:
  • Stay at the forefront of AI and machine learning advances, bringing research into production environments.
  • Evaluate and integrate emerging techniques such as LLMs, reinforcement learning, and generative AI where applicable.
Mentorship & Collaboration:
  • Mentor senior ML engineers and data scientists on technical best practices, system design, and experimentation methodology.
  • Partner with product and data teams to translate business challenges into AI-driven solutions.
Execution & Delivery:
  • Lead the technical roadmap for AI systems, ensuring timely delivery of models and services with measurable outcomes.
  • Establish standards for model testing, explainability and monitoring.
Must-Have:
  • 10+ years of experience in software engineering or data science, with at least 3+ years in applied AI/ML leadership roles.
  • Proven track record of architecting, deploying, and maintaining production-scale ML systems.
  • Strong programming skills in Python and proficiency with ML frameworks (PyTorch, TensorFlow, etc.).
  • Experience with large-scale data systems and ML pipelines (Kubeflow, Airflow, Vertex AI, or SageMaker).
  • Deep understanding of machine learning fundamentals — model training, evaluation, and serving.
  • Expertise in cloud-based ML infrastructure (AWS, GCP, or Azure) and containerization (Kubernetes, Docker).
  • Excellent communication skills and ability to influence technical direction across multiple teams.
Nice-to-Have:
  • Experience with LLMs, transformer architectures, vector databases, and retrieval-augmented generation (RAG).
  • Understanding of AI safety, model interpretability, and responsible AI principles.
  • Publications, patents, or open-source contributions in AI/ML.
  • Background in applied domains such as NLP, computer vision, or recommendation systems.

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