AI Engineer

At DevSavant, we are a trusted technology partner delivering cutting-edge solutions across Software Development, Data Engineering, AI/Machine Learning, Cloud Services, Automation Testing, and UI/UX Design. We are driven by a commitment to excellence and measurable results. Our team is our greatest asset—we foster a culture of growth, collaboration, and well-being. Join us and thrive in an environment designed for success.

Our client offers a cloud-based, AI-powered platform that helps accounting, tax, audit, and bookkeeping professionals streamline operations and enhance productivity through an all-in-one solution.

We are looking for an experienced AI Engineer (3–5 years) to help design and implement intelligent features across the platform.

Job Overview:
The AI Engineer will be responsible for designing, developing, and deploying AI-driven solutions to enhance the platform. You will work closely with cross-functional teams, including product managers, data scientists, and software engineers, to implement machine learning models, optimize AI systems, and integrate these capabilities into our cloud-based platform. You will have the opportunity to contribute to meaningful AI innovations that improve workflows, automate processes, and deliver measurable value to our customers.

This is a remote work-from-home position with occasional travel required for team meetings, conferences, and client engagements.

Key Responsibilities:

AI Model Development
Design, develop, and implement machine learning models and AI algorithms to solve business problems within the practice management space.

Work on enhancing features such as document management, data automation, and predictive analytics.

Design and implement Generative AI solutions using Large Language Models (LLMs) for document understanding, summarization, and automation use cases.

Develop Retrieval-Augmented Generation (RAG) workflows to support intelligent document search and retrieval.

Data Preprocessing and Feature Engineering:
Perform data preprocessing tasks such as cleaning, transformation, and feature engineering to prepare datasets for model training.

Implement data pipelines that support embedding generation for vector search systems.

AI Model Training and Optimization

Train machine learning and LLM-based models on large datasets, tune hyperparameters, and optimize performance.

Evaluate models in terms of accuracy, speed, and contextual relevance when used in RAG-based architectures.

AI Integration:
Collaborate with software engineers to integrate LLMs, RAG workflows, and other AI systems into the platform.

Integrate vector databases (e.g., Pinecone, Weaviate) for enhanced search and retrieval functionality.

Collaboration with Cross-functional Teams:
Work closely with data scientists, product managers, and software engineers to align AI solutions with customer needs and business goals.

Contribute to product features by providing insights and expertise on AI technologies.

Continuous Learning and Innovation:
Stay up to date with the latest AI trends, tools, and technologies.

Implement cutting-edge solutions and participate in AI research to improve the quality and impact of AI features.

Performance Monitoring and Metrics:
Track the performance of AI models and AI-powered features after deployment.

Use metrics to assess effectiveness and identify areas for continuous improvement.

Qualifications:

• Experience:
  3–5 years of experience in AI, machine learning, or data science, with a strong track record of developing and deploying machine learning models. Experience working with cloud-based platforms (AWS, Google Cloud, etc.) is a plus.

• Hands-on experience with LLMs (e.g., OpenAI, Cohere, Claude, LLaMA) and prompt engineering.

• Experience working with vector databases (e.g., Pinecone, Qdrant, Weaviate) and knowledge of embedding generation and retrieval techniques.

• Proven ability to build or integrate Retrieval-Augmented Generation (RAG) architectures in production environments.

• Practical experience with document-based AI workflows, including OCR, structured document extraction, and semantic search.

• Technical Expertise:
  Proficiency in machine learning algorithms, deep learning, and natural language processing (NLP). Experience with popular AI frameworks such as TensorFlow, PyTorch, Scikit-learn, and Keras.

• Programming Skills:
  Strong coding skills in Python (or other relevant programming languages) and familiarity with data manipulation libraries such as NumPy, Pandas, and Matplotlib. Knowledge of SQL and experience working with databases is a plus.

• Data Handling:
  Strong understanding of data preprocessing, feature engineering, and model validation techniques. Ability to work with large datasets and implement efficient data pipelines.

• Cloud & AI Integration:
  Experience with deploying and integrating machine learning models into cloud platforms (AWS, Google Cloud, Azure) and knowledge of containerization tools like Docker is beneficial.

• Problem-Solving Skills:
  Strong analytical skills and a problem-solving mindset. Ability to approach complex challenges with creative and efficient AI-driven solutions.

• Education:
  A Bachelor's degree in Computer Science, Data Science, Engineering, or a related field is required. A Master’s degree or certification in AI/ML is a plus.