Smithfield RI 02917 Contract is only for 4-months. $90-92/hr C2C or $80/hr on W2 or $90-92 on 1099 Client is Fidelity
****MUST LIST WHAT CANDIDATE HAS SPECIFICALLY DONE WITH ALL 4 SKILLS MENTIONED BELOW IN DETAIL****
Must Have: - AWS - Docker - Kubernetes - Python - Excellent communication skills
Principal Software Engineer (Machine learning and analytics platform model engineer) As a team member, you will be responsible to support data scientist and other ML engineers through their evolving need to manage AI machine learning model life-cycles in partnership with other cross functional teams. You have clear understanding of AI models built on Python with AI frameworks, expert understanding of REST APIs, and have expertise with Kubernetes, Docker containerization, and deployment to AWS services. You will support our data scientist, model engineers, model DevOps, and data analysts to build an optimal pipeline. You are self-directed and comfortable supporting the ML Ops needs of multiple stakeholders, systems and products. You will also be responsible for aligning them with the proposed architecture. The right candidate will be excited by the prospect to build out analytics platform bottoms up to support our existing and next generation of ML-driven products and solutions initiatives.
The Purpose of Your Role We are undertaking big initiatives to transform our compliance, surveillance and security business partner's ability to improve the effectiveness of firm-wide compliance surveillance processes to reduce false positives, improve detection, and to keep pace with regulatory expectations for surveillance. This we plan to achieve by bringing multiple AI models to production over the course of this year. The role's purpose will be to help stand up the exploration, challenge and production pipelines for AI models working with data scientist, architecture and Enterprise cloud computing teams.
Responsibilities: Design and build Docker containers to host data recipe, model, and performance analysis Establish patterns that are repeatable for building data artifact containers, model artifact containers, and performance analysis containers in exploration, challenge and production lanes. Use your experience in many of the following: AWS, EKS, ECS, EMR, EC2, S3, Azure, Google, Concourse, Artifactory, GIT Clear understanding of Elastic scaling on the cloud and the management/tuning of resources on the cloud Kubernetes, Docker, PCF Python scripts, objects, and modules including knowledge of creating and distribution of code built using them. Google Tensorflow, Tensor serving, H2O.ai, and other similar frameworks Shell scripting, Curl, Java, C++, Hadoop, Yarn, PL/SQL, Parquet files, Spark, Knowledge of data wrangling/data recipe preparation tools such as Trifacta and model interface performance management tools such as FastCore is a plus Use monitoring resources and to measure and recommend optimal deployment configurations Use your experience with model life-cycle management to recommend optimal solutions for the enterprise Qualifications:
Experience containerizing and deploying applications and services to cloud platforms such as Amazon Web Services (AWS), Azure, and Google cloud using Kubernetes and Dockers. Current experience managing life-cycle of machine learning models deployed to the cloud using CI/CD tools such as Concourse. Experience with AI model physical architecture and an ability to interact with architects to arrive at solutions will be key. Prior experience supporting data scientist and building models using Python, Tensor flow, H2O.AI etc. is required. Candidates will be required to possess the necessary organizational and leadership skills to work with multiple cross functional teams across the enterprise. Being a highly motivated and responsive team player who can multi-task and work under minimal supervision is a must.
Previous experience: Containerize AI ML model artifacts from data scientist into containers that can be deployed into training, challenge, or production deployment pipelines supporting data recipe, model, and performance containers. Deployment expertise of models on AWS EKS platform at scale Strong understanding of AWS services including EC2, S3 Buckets, EKS Hands on experience with Docker and Kubernetes for the cloud are a must. Knowledge of model data set staging and familiarity integrating with big data sets that meet functional and non-functional business requirements. Proficiency with packaging Python, Python objects, Python modules, Tensor flow, Tensor flow serving, Fastex, H20.ai and other ML enabling tools. Knowledge/proficiency with technologies such as Hadoop, Yarn, Trifacta, EMR, FastCore etc. Work with stakeholders including the Product, Data scientist, business, and design teams to assist with technical issues and support their pipeline needs. Critical awareness in handling procedures for confidential and highly confidential data sets Create toolsets for analytics and data scientist team members that assist them in building and optimizing our product and models into an innovative leader in this space. Work with data, business and machine learning experts for greater functionality in our data and model life-cycle Ability to handle multiple priorities in an agile delivery model A \"Can Do\" attitude and ability to work in a highly matrixed and global team Excellent written and oral communication skills Fundamental knowledge of data science, Machine learning and deep learning Excellent problem-solving skills
Special Instructions: Machine Learning Software Engineer - Smithfield, RI (159513)
Submission Format: Legal Name: Phone Number: Email Address: Last 4 of SSN: MM/DD of birth: Visa Status: Location: Availability to interview: Availability to start: Bio: Skill Highlights- Provide the # years the candidate has on each of the following skills: - AWS Deployment expertise of models on AWS EKS platform at scale Strong understanding of AWS services including EC2, S3 Buckets, EKS - Docker - Kubernetes - Python Big Data Hadoop Preferred: Yarn Trifacta EMR FastCore.
Any relevant Certifications? Other points that make this candidate a great fit for the role:
- provided by Dice Associated topics: back end, c, design, java, matlab, maven, perl, programming, project architect, senior
* The salary listed in the header is an estimate based on salary data for similar jobs in the same area. Salary or compensation data found in the job description is accurate.