Custom Software Engineer

Role Overview

Develop custom software solutions to design, code, and enhance components across systems or applications. Use modern frameworks and agile practices to deliver scalable, high-performing solutions tailored to specific business needs. As a Custom Software Engineer, a typical day involves engaging deeply with various stages of software development tailored to client needs. This includes analyzing requirements, designing solutions, writing and testing code for multiple application components, and ensuring the software meets quality standards.

Responsibilities

• Focus on consuming, integrating, and operationalizing advanced AI models—from Large Language Models (LLMs) to Small Language Models (SLMs)—into secure, governed, and scalable business solutions.
• Enforce data-handling policies in code (prompt redaction middleware, retrieval allow-lists, per-use-case policies).
• Add evaluation gates (answer quality, safety) to pipelines, canary deploys with feature flags, and automated rollback on drift.
• Manage SLM/LoRA fine-tuning with consented datasets, synthetic augmentation policies, and model registry entries with lineage.
• Implement observability including tracing (request, retrieved docs, model output, tool calls) and latency & cost SLOs alerts on hallucination/safety incidents.
• Wrap OpenAI/Gemini/Azure OpenAI/Vertex behind an interface to capture provider/region, model/version, and quota routing.
• Manage RAG Ops: schedule index rebuilds, freshness windows, incremental sync, and search quality dashboards.
• Develop APIs and microservices to integrate AI with internal/external applications.
• Partner with business stakeholders to identify and validate use cases.

Requirements

• Minimum 3 years of experience in Machine Learning (ML).
• Strong knowledge of machine learning algorithms and their practical applications.
• Experience with data preprocessing techniques to prepare datasets for modeling.
• Ability to implement and optimize models for classification, regression, and clustering tasks.
• Familiarity with software development lifecycle and version control systems.
• Capability to analyze and troubleshoot issues in machine learning pipelines and software components.
• 15 years of full-time education.

Skills

• Machine Learning (ML)
• Large Language Models (LLMs)
• Small Language Models (SLMs)
• RAG Ops
• Python