AI Engineer - SE II (3-6 years)

AI Engineer 

Location: Bangalore

Reports to: Director AI Engineering

Department: AI Accelerate

About Us: Welcome to Foundry, a cryptocurrency pioneer dedicated to advancing decentralized infrastructure. At Foundry, we’re not waiting for a decentralized financial future — we’re building it now. By empowering institutions with the tools, they need to mine and stake digital assets, we’re thoughtfully driving the industry forward with our team at the centre of it all. https://foundrydigital.com/

Job Description: 

Role Overview: We are looking for a data driven AI Engineer who loves to develop GenAI applications, forecasting, advanced natural language processing solutions for our mining operations. The ideal candidate will work on projects such as predictive forecasting, text processing, Human/AI text detection and text analysis of mining data. This role requires a deep understanding of machine learning and language processing techniques.

Key Responsibilities: 

• Model Implementation: Design, build, enhance batch and real-time inference services and tooling that support our ML use cases. Ensure the models are scalable, reproducible, and maintainable. Analyse and understand the idiosyncratic characteristics of mining flow and optimize execution algorithms
• Collaboration: Work with Senior Data Scientist and Product managers to produce mining models and refine existing algorithms. Develop prototypes and partner with ML modelers to encourage adoption of new tools and technologies and plan for future needs of our ML teams. Contribute to end-to-end research and implementation of execution algorithms, including idea generation, data collection, cleaning, model development, evaluation and improvement
• Documentation: Maintain thorough documentation for mining model workflows, infrastructure setups, and deployment
• CI/CD Pipelines: Develop and maintain CI/CD pipelines tailored for machine learning
• Containerization and Orchestration: Use Docker for containerizing machine learning applications and Kubernetes for orchestrating and managing containerized.

Requirements: 

• Experience: 3-6 years of experience in Data Science, or a related role with a strong focus on machine learning model development and deployment.
• Python: Proficiency in Python for scripting, building, automation, and integrating machine learning models into production environments.
• Strong programming skills in Python and experience with NLP libraries (e.g., NLTK, spaCy, etc.)
• Machine Learning Frameworks: Proficiency in machine learning frameworks such Huggingface, PyTorch, Tensorflow, etc.

• Experience in Research statistical techniques such as time-series methods, stochastic methods, etc.
• Experience with deep learning and LLM models for NLP tasks (e.g., BERT, GPT, GPT)
• Knowledge of text classification, named entity recognition and sentiment analysis
• Problem-Solving: Excellent troubleshooting skills with a proactive approach to resolving
• Version Control: Experience with version control systems, particularly
• Cloud Platforms: Experience with cloud services (AWS, Azure, GCP) and cloud- native tools for managing infrastructure and
• CI/CD Tools: Proficiency with CI/CD tools such as Jenkins, GitLab CI, or
• MLOps Tools: Experience with MLOps frameworks and tools such as MLflow, Kubeflow, or similar.
• Communication: Strong communication skills and the ability to work effectively in a collaborative team
• Collaboration: Ability to work well in collaborative and high pace settings, and drive projects to completion in accelerated.

Preferred Qualifications: 

• A Bachelor's degree in computer science, data science, operations research, applied math, stats, physics, or related technical field
• 3+ years of combined Machine Learning and Engineering industry experience (full stack ML experience)
• Familiarity with Linux/OS X command line, version control software (git), and software development principles with a machine learning software development life- cycle
• Experience working with product, business, and engineering to prioritize, scope, design, and deploy ML models
• Familiarity with Python computing stack, MySQL, Snowflake, Airflow
• Hosted models for inference on public clouds like GCP, AWS and/or built micro- services to facilitate event based triggering, feature generation, model inference and downstream
• Good To Have: Quantitative background - includes advanced degrees in computer science, machine learning / NLP, statistics, signal processing, optimization, mathematics and related STEM subjects (masters or higher)

•

Last updated on Aug 20, 2024