What are the responsibilities and job description for the Senior Machine Learning Engineer position at Hatch Global Search?
Job Description
Senior Machine Learning Engineer
New York, NY, United States
Qualifications
The candidate will have an excellent opportunity to work in the cutting-edge field of machine learning and data science
Strong work experience as a ML engineer or data scientist (5 years) Strong work experience as a ML engineer or data scientist (5 years)
Prior experience developing production quality Python code is essential
High level of proficiency in machine learning and and deep learning algorithms such as multi-class classifications, decision trees, support vector machines,
Strong adherence to software and ML development fundamentals (e.g. code quality considerations, automated testing, source version control, optimization)
Prior experience working on building Gen AI frameworks and leveraging and / or finetuning LLM's
Experience building / enhancing search and information retrieval systems
Exposure / experience in containerization technologies like docker, Kubernetes, AWS EKS etc
Knowledge of popular Cloud computing vendor (AWS and Azure) infrastructure & services e.g
Master's degree or above in Machine learning / data science, computer science, applied mathematics or otherwise research-based field
Passionate about the power of data and predictive analytics to drive better business outcomes for customers
Proven ability to work effectively in a distributed working environment and ability to work efficiently and productively in a fast-paced environment
Familiarity with credit ratings agencies, regulations, and data products around the world
Outstanding written and verbal communication skills
A champion of good code quality and architectural practices
Strong interpersonal skills and ability to work proactively and as a team player
Benefits
This will be a high impact role with significant visibility where the candidate will work on some flagship products
Company promotes an excellent work culture and is known for providing a good work life balance
FOR NEW YORK ROLES ONLY : Expected base pay salary range for this role is $160,000 - $180,000 per year
Actual salaries will be determined on an individualized basis and may vary based on factors including but not limited to education, training, experience, past performance, and other job-related factors
Base pay is one part of this company's total compensation package, which, depending on the position, may also include commission earnings, discretionary bonuses, long-term incentives, and other benefits sponsored by the company
Responsibilities
Gen AI / ML technology implementation with business and product owners
Emerging Tech Governance function covering policies, guidelines and processes to govern AI / ML enabled components as well as third party AI governance
Lead and support other enterprise-level AI exploration tools and capabilities
Provide guidance and support for safe development and deployment of AI
Work closely / as part of the product squads to build, integrate, and deploy GenAI and data science solutions
Ensure sharing best practices and learnings with other squad members
Effectively communicate advanced data science / ML concepts in simple language to the business stakeholders always focusing on its applicability to Fitch business
Develop and deploy machine learning and gen AI solutions to meet enterprise goals and support experimentation and innovation
Collaborate with data scientists to identify innovative machine learning and Gen AI solutions that leverage data to meet business goals
Designs and develops scalable solutions and ML workflows that leverage machine learning, gen AI and deep learning models to meet enterprise requirements
Actively participate in maintaining SLAs for Production applications and support
Create metrics to continuously evaluate the performance of machine learning solutions and improve the performance of existing machine learning solutions
Build with AWS and Azure cloud computing services providing the necessary infrastructure, resources, and interfaces to enable data loading and LLM workflows
Use Python and large-scale data workflow orchestration platforms e.g
Airflow to construct software artifacts for ETL, interfacing with diverse data formats and storage technologies, and incorporate them into robust data workflows and dynamic systems
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