Senior AI Engineer
RNS Solutions
Islamabad, pk
23h ago

Job Description

We are looking for a Senior AI engineer to join our team. We are interested in individuals with knowledge and experience in developing and evaluating different reinforcement learning (RL) architectures and algorithms, and in their applications in experimentation and causal inference.

You will have the unique opportunity of defining and building from scratch reinforcement learning features for our next-generation machine learning platform for equitable fin-tech.

Responsibilities :

Working on the reinforcement learning algorithms that will run in our platform : definition, initial implementation, evaluation and deployment

Formulate real-world causal inference and experimental design problems in terms of different RL algorithms, and independently conduct research to decide upon the best approach.

Requirements

Minimum Qualification

  • BSc / BEng degree in computer science, mathematics, physics, electrical engineering, machine learning or related fields;
  • or equivalent technical proficiency

    Experience in reinforcement learning and its libraries (Keras-rl, TF-agents, garage, Pyqlearning, ChainerRL )

    Solid coding skills in at least one of the following languages : Go, Scala, Python

    Preferred Qualification

  • Knowledge of RL applications in FinTech, stocks and time series data analysis!
  • Experience using a variety of RL algorithms : actor-critic, policy gradient, DQN, VFA, SARSA, Q-learning, model-based and model-free Monte Carlo, dynamic programming...
  • Experience with deep learning and its frameworks (TensorFlow, Keras, Caffe, Theano...)
  • Experience with recommendation systems and / or natural language processing
  • Strong statistical background
  • Experience with machine learning lifecycle management tools (e.g. MLflow)
  • Experience with continuous deployment of models with build pipeline
  • Experience with databricks
  • Experience with Spark performance tuning, data pipeline testing and MLlib
  • Benefits

    RNS Solutions offers an outstanding culture that focuses on learning opportunities, international exposure by participation at international and national conferences, and career growth.

    Marketing competitive salary

    Provident fund

    Health and maternity coverage

    Sumptuous, home-cooked lunch (Free)

    Accommodation for outstation employees (subsidized)

    Expert-led fitness training (subsidized)

    Performance bonuses and annual Increments

    EOBI membership

    Chances to travel abroad

    Sponsored Certifications

    Mentoring and grooming

    Pakistan Office

    3rd Floor, Al-Rahim Arcade, National Market, Satellite Town, Rawalpindi, Pakistan

    Singapore Office

    531 A Upper Cross Street #04-95, Hong Lim Complex, Singapore. Location

    Korea Office

    Hanyang Building 59-1 Gangnam-daero 6-gil, Seocho-gu, Seoul (Yangjae-dong) Location

    Saudi Arabia Office

    7198 Uthman Ibn Affan Rd, Al Nada, Riyadh 4415, Saudi Arabia

    Requirements

  • Minimum Qualification BSc / BEng degree in computer science, mathematics, physics, electrical engineering, machine learning or related fields;
  • or equivalent technical proficiency Experience in reinforcement learning and its libraries (Keras-rl, TF-agents, garage, Pyqlearning, ChainerRL ) Solid coding skills in at least one of the following languages : Go, Scala, Python Preferred Qualification Knowledge of RL applications in FinTech, stocks and time series data analysis! Experience using a variety of RL algorithms : actor-critic, policy gradient, DQN, VFA, SARSA, Q-learning, model-based and model-free Monte Carlo, dynamic programming.

  • Experience with deep learning and its frameworks (TensorFlow, Keras, Caffe, Theano...) Experience with recommendation systems and / or natural language processing Strong statistical background Experience with machine learning lifecycle management tools (e.
  • g. MLflow) Experience with continuous deployment of models with build pipeline Experience with databricks Experience with Spark performance tuning, data pipeline testing and MLlib

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