Azure AI Engineer

Role: Azure AI Engineer Location: Washington, DC Duration: 5 Months 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): • 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 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. “Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of – Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.”