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AI/ML Strategist

2 weeks ago

Noida, Uttar Pradesh, India beBeeArtificialIntelligence Full time ₹ 2,00,00,000 - ₹ 2,50,00,000

We are seeking an experienced AI/ML Strategist to lead and shape AI/ML initiatives.

The ideal candidate will have hands-on experience in machine learning and artificial intelligence, with strong leadership capabilities and a passion for delivering production-ready solutions.

This role involves end-to-end ownership of AI/ML projects, from strategy development to deployment and optimization of large-scale systems.

Key Responsibilities:

  • Develop and execute AI/ML strategies aligned with business goals.
  • Collaborate with product and engineering teams to identify impactful AI/ML opportunities.
  • BUILD, TRAIN, TUNE AND DEPLOY MACHINE LEARNING MODELS IN PRODUCTION ENVIRONMENTS
  • MANAGE OPERATIONS OF LLMs AND OTHER AI/ML MODELS USING MODERN CLOUD AND MLOPS TOOLS.
  • IMPLEMENT SCALABLE AND AUTOMATED AI/ML PIPELINES (E.g., WITH KUBEFLOW OR MLRUN).
  • HANDLE CONTAINERIZATION AND ORCHESTRATION USING DOCKER AND KUBERNETES.
  • OPTIMIZE GPU/TPU RESOURCES FOR TRAINING AND INFERENCE TASKS.
  • DEVELOP EFFICIENT RAG PIPELINES WITH LOW LATENCY AND HIGH RETRIEVAL ACCURACY.
  • AUTOMATE CI/CD WORKFLOWS FOR CONTINUOUS INTEGRATION AND DELIVERY OF AI/ML SYSTEMS.

Requirements:

  • Proficiency in AWS, Google Cloud, or Azure for scalable model deployment.
  • Familiarity with cloud-native services like AWS SageMaker, Google Vertex AI, or Azure ML.
  • Expertise in Docker and Kubernetes for containerized deployments.
  • Experience with Infrastructure as Code (IaC) using tools like Terraform or CloudFormation.
  • Strong command of frameworks: TensorFlow, PyTorch, Scikit-learn, XGBoost.
  • Experience with MLOps tools for integration, monitoring, and automation.
  • Expertise in pre-trained models, transfer learning, and designing custom architectures.
  • Strong skills in Python (NumPy, Pandas, Matplotlib, SciPy) for ML development.
  • Backend/API development with FastAPI, Flask, or Django.
  • Database handling with SQL and NoSQL (PostgreSQL, MongoDB, BigQuery).
  • Familiarity with CI/CD pipelines (GitHub Actions, Jenkins).
  • Proven ability to build AI/ML-driven applications at scale.
  • Handle large datasets, high-throughput requests, and real-time inference.
  • Knowledge of distributed computing: Apache Spark, Dask, Ray.
  • Hands-on with model compression, quantization, and pruning.
  • A/B testing and performance tracking in production.
  • Knowledge of model retraining pipelines for continuous learning.
  • Efficient use of compute resources: GPUs, TPUs, CPUs.
  • Experience with serverless architectures to reduce cost.
  • Auto-scaling and load balancing for high-traffic systems.
  • Translate complex ML models into user-friendly applications.
  • Work effectively with data scientists, engineers, and product teams.
  • Write clear technical documentation and architecture reports.