AIML Engineer

3 - 5 years

16 - 20 Lacs

Posted:1 month ago| Platform: Foundit logo

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Work Mode

On-site

Job Type

Full Time

Job Description

Position Overview

AI/ML Engineer

Key Responsibilities

  • Lead and mentor a high-performing AI/ML team.
  • Design and execute AI/ML strategies aligned with business goals.
  • Collaborate with product and engineering teams to identify impactful AI opportunities.
  • Build, train, fine-tune, and deploy ML models in production environments.
  • Manage operations of LLMs and other AI models using modern cloud and MLOps tools.
  • Implement scalable and automated 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 ML systems.

Key Skills & Expertise

1. Cloud Computing & Deployment

  • 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.

2. Machine Learning & Deep Learning

  • 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.

3. Programming & Software Engineering

  • 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).

4. Scalable AI Systems

  • Proven ability to build AI-driven applications at scale.
  • Handle large datasets, high-throughput requests, and real-time inference.
  • Knowledge of distributed computing:

    Apache Spark, Dask, Ray

    .

5. Model Monitoring & Optimization

  • Hands-on with

    model compression, quantization, and pruning

    .
  • A/B testing and performance tracking in production.
  • Knowledge of model retraining pipelines for continuous learning.

6. Resource Optimization

  • Efficient use of compute resources:

    GPUs, TPUs, CPUs

    .
  • Experience with

    serverless architectures

    to reduce cost.
  • Auto-scaling and load balancing for high-traffic systems.

7. Problem-Solving & Collaboration

  • Translate complex ML models into user-friendly applications.
  • Work effectively with data scientists, engineers, and product teams.
  • Write clear

    technical documentation and architecture reports

    .

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