Sr AI / ML Software Engineer - Onsite interview in MD.

About Unisoft Technology Inc

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Requirements

• Bachelor of Science in Engineering, Computer Science, Data Science, or Mathematics, or a related field
• At least Seven (7) years’ experience in data science, machine learning, or applied AI development
• At least Seven (7) years’ experience in software engineering, architecture, or web development.

Hands on real time experience with:

• SQL and relational database systems (e.g., PostgreSQL)
• Fine-tuning small language models or embedding models
• Contributing to or maintaining open-source software projects
• Graph databases or graph extensions (e.g., Neo4j, Apache AGE)
• Designing and implementing multi-agent or task-oriented AI systems
• Embedding models, vector similarity, re-ranking, and graph retrieval techniques in RAG systems
• Version control systems (e.g., Git), containerization technologies (e.g., Docker), and service-oriented architectures
• Collaborating with large language models (LLMs), including both API-based integration and local deployment
• Validating AI-generated outputs, mitigating hallucinations, and integrating AI tools into production service pipelines

Ability to:

• Understand data structures, algorithms, and clean coding principles
• Select and apply appropriate techniques (LLM and non-LLM) based on task.
• Develop and improve testing and evaluation pipelines for AI systems, including use of synthetic data
• Demonstrate proficiency in Python, including the ability to develop production grade backend services, APIs, middleware, and data pipelines.
• Design and implement AI/ML systems that operate effectively on complex, inconsistent, or evolving datasets while balancing accuracy, latency, and cost (token consumption)
• Collaborate with team members to define system architecture, agent workflows, and data pipelines while working in constrained environments, including limited GPU availability and predefined infrastructure

Knowledge of:

• Hybrid cloud environments and distributed system considerations
• Threading, asynchronous processing, and queues in backend servers
• React and Microsoft Teams Toolkit for developing chatbot user interfaces
• Non-llm data analysis techniques for structured, semi-structured, and unstructured data
• Classical natural language processing (NLP) techniques in addition to LLM-based approaches
• Data science and LLM-related libraries in Rust or other performance-oriented programming languages