Mindlance
Base pay range
$67.00/hr - $72.00/hr
Actively Hiring in DMV Area- Largest Financial Institutions Location:
Washington, DC
Duration:
7 Months with long term extension
Hybrid Onsite:
4 days per week from Day 1, with a full transition to 100% onsite anticipated soon.
AI Engineer: The AI Engineer will play a pivotal role in designing, developing, and deploying artificial intelligence solutions that enhance operational efficiency, automate decision-making, and support strategic initiatives for the environmental and social specialists within the client. This role is central to the VPU’s digital transformation efforts and will contribute to the development of scalable, ethical, and innovative AI systems.
Qualifications and Experience Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field.
Experience:
Minimum 3 years of hands on experience in AI/ML model development and deployment.
Experience with MLOps tools (e.g., MLflow), Docker, and cloud platforms (AWS, Azure, GCP).
Proven track record in implementing LLMs, RAG, NLP model development and GenAI solutions.
Technical Skills:
Skilled in –
Azure AI/Google Vertex Search, Vector Databases, fine-tuning the RAG, NLP model development, API Management
(facilitates access to different sources of data)
Proficiency in
Python, TensorFlow, PyTorch, and NLP
frameworks.
Expertise deep learning, computer vision, and large language models.
Familiarity with REST APIs, NoSQL, and RDBMS.
Certifications (Preferred):
Google Machine Learning Engineer
SAFe Agile Software Engineer (ASE)
Certification in AI Ethics
Objectives of the Assignment:
Develop and implement AI models and algorithms tailored to business needs.
Integrate AI solutions into existing systems and workflows.
Ensure ethical compliance and data privacy in all AI initiatives.
Support user adoption through training and documentation.
Support existing AI solutions by refinement, troubleshooting, and reconfiguration
Scope of Work and Responsibilities: AI Solution Development:
Collaborate with cross-functional teams to identify AI opportunities.
Train, validate, and optimize machine learning models.
Translate business requirements to technical specifications.
AI Solution Implementation:
Develop code, deploy AI models and into production environments, and conduct ongoing model training
Monitor performance and troubleshoot issues and engage in fine-tuning the solutions to improve accuracy
Ensure compliance with ethical standards and data governance policies.
User Training and Adoption:
Conduct training sessions for stakeholders on AI tools.
Develop user guides and technical documentation.
Data Analysis and Research:
Collect, preprocess, and engineer large datasets for machine learning and AI applications.
Recommend and Implement Data Cleaning and Preparation
Analyse and use structured and unstructured data (including geospatial data) to extract features and actionable insights.
Monitor data quality, detect bias, and manage model/data drift in production environments.
Research emerging AI technologies and recommend improvements.
Governance, Strategy, Support, and Maintenance:
Advise client's staff on AI strategy and policy implications
Contribute to the team’s AI roadmap and innovation agenda.
Provide continuous support and contribute towards maintenance and future enhancements.
Work on Proof of Concepts to study the technical feasibility of AI Use Cases
Functional AI applications integrated into business systems.
Documentation of model/application architecture, training data, and performance metrics.
Training materials and user guides.
Develop, train, and deploy AI models tailored to business needs
“Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of – Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.”
Seniority level Mid-Senior level
Employment type Contract
Job function Information Technology
Industries Banking and Financial Services
#J-18808-Ljbffr
Actively Hiring in DMV Area- Largest Financial Institutions Location:
Washington, DC
Duration:
7 Months with long term extension
Hybrid Onsite:
4 days per week from Day 1, with a full transition to 100% onsite anticipated soon.
AI Engineer: The AI Engineer will play a pivotal role in designing, developing, and deploying artificial intelligence solutions that enhance operational efficiency, automate decision-making, and support strategic initiatives for the environmental and social specialists within the client. This role is central to the VPU’s digital transformation efforts and will contribute to the development of scalable, ethical, and innovative AI systems.
Qualifications and Experience Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field.
Experience:
Minimum 3 years of hands on experience in AI/ML model development and deployment.
Experience with MLOps tools (e.g., MLflow), Docker, and cloud platforms (AWS, Azure, GCP).
Proven track record in implementing LLMs, RAG, NLP model development and GenAI solutions.
Technical Skills:
Skilled in –
Azure AI/Google Vertex Search, Vector Databases, fine-tuning the RAG, NLP model development, API Management
(facilitates access to different sources of data)
Proficiency in
Python, TensorFlow, PyTorch, and NLP
frameworks.
Expertise deep learning, computer vision, and large language models.
Familiarity with REST APIs, NoSQL, and RDBMS.
Certifications (Preferred):
Google Machine Learning Engineer
SAFe Agile Software Engineer (ASE)
Certification in AI Ethics
Objectives of the Assignment:
Develop and implement AI models and algorithms tailored to business needs.
Integrate AI solutions into existing systems and workflows.
Ensure ethical compliance and data privacy in all AI initiatives.
Support user adoption through training and documentation.
Support existing AI solutions by refinement, troubleshooting, and reconfiguration
Scope of Work and Responsibilities: AI Solution Development:
Collaborate with cross-functional teams to identify AI opportunities.
Train, validate, and optimize machine learning models.
Translate business requirements to technical specifications.
AI Solution Implementation:
Develop code, deploy AI models and into production environments, and conduct ongoing model training
Monitor performance and troubleshoot issues and engage in fine-tuning the solutions to improve accuracy
Ensure compliance with ethical standards and data governance policies.
User Training and Adoption:
Conduct training sessions for stakeholders on AI tools.
Develop user guides and technical documentation.
Data Analysis and Research:
Collect, preprocess, and engineer large datasets for machine learning and AI applications.
Recommend and Implement Data Cleaning and Preparation
Analyse and use structured and unstructured data (including geospatial data) to extract features and actionable insights.
Monitor data quality, detect bias, and manage model/data drift in production environments.
Research emerging AI technologies and recommend improvements.
Governance, Strategy, Support, and Maintenance:
Advise client's staff on AI strategy and policy implications
Contribute to the team’s AI roadmap and innovation agenda.
Provide continuous support and contribute towards maintenance and future enhancements.
Work on Proof of Concepts to study the technical feasibility of AI Use Cases
Functional AI applications integrated into business systems.
Documentation of model/application architecture, training data, and performance metrics.
Training materials and user guides.
Develop, train, and deploy AI models tailored to business needs
“Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of – Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.”
Seniority level Mid-Senior level
Employment type Contract
Job function Information Technology
Industries Banking and Financial Services
#J-18808-Ljbffr