Carlsbad Tech is hiring: AI Engineer at Akaasa Technologies Washington DC in Was
Carlsbad Tech, Washington, DC, United States
AI Engineer job at Akaasa Technologies. Washington DC.
2-4 DAYS ON SITE
AI Engineer
Background and Context
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 in the World Bank Group. 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
Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field.
Minimum 3 years of 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 RAG, NLP model development, API Management.
Proficiency in Python, TensorFlow, PyTorch, and NLP frameworks.
Expertise in deep learning, computer vision, and large language models.
Familiarity with REST APIs, NoSQL, and RDBMS.
Soft Skills
Strong analytical and problem‑solving abilities.
Excellent communication and teamwork skills.
Strategic thinking and innovation mindset.
Certifications (Preferred)
Microsoft Certified: Azure AI Engineer Associate
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 into production environments, and conduct ongoing model training.
Monitor performance, troubleshoot issues, and fine‑tune 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.
Analyze 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 WBG 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.
Deliverables
Work on Proof of Concepts to study the technical feasibility of AI Use Cases.
[MP1] Develop, train, and deploy AI models tailored to business needs.
#J-18808-Ljbffr
2-4 DAYS ON SITE
AI Engineer
Background and Context
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 in the World Bank Group. 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
Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field.
Minimum 3 years of 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 RAG, NLP model development, API Management.
Proficiency in Python, TensorFlow, PyTorch, and NLP frameworks.
Expertise in deep learning, computer vision, and large language models.
Familiarity with REST APIs, NoSQL, and RDBMS.
Soft Skills
Strong analytical and problem‑solving abilities.
Excellent communication and teamwork skills.
Strategic thinking and innovation mindset.
Certifications (Preferred)
Microsoft Certified: Azure AI Engineer Associate
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 into production environments, and conduct ongoing model training.
Monitor performance, troubleshoot issues, and fine‑tune 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.
Analyze 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 WBG 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.
Deliverables
Work on Proof of Concepts to study the technical feasibility of AI Use Cases.
[MP1] Develop, train, and deploy AI models tailored to business needs.
#J-18808-Ljbffr