Mid-Level GenAI Engineer

Job Description

We're looking for a Junior-Mid Generative AI Engineer (typically 1-5 years of experience) with strong Python, solid computer science / computer engineering fundamentals, and hands-on experience building and deploying AI-enabled applications. You'll partner with product, engineering, and data stakeholders to design, prototype, and productionize LLM-powered agentic workflow. This role is ideal for someone who enjoys shipping production software, writing clean code, and learning quickly in a rapidly evolving GenAI ecosystem-while applying disciplined engineering practices (testing, observability, security, performance).

Key Responsibilities

Build & Deliver GenAI Features

• Implement and iterate on GenAI capabilities such as: Retrieval-Augmented Generation (RAG), semantic search, summarization, classification, and workflow automation

• Design prompt strategies and guardrails (e.g., structured outputs, prompt templates, evaluation criteria)

• Integrate LLMs into applications via APIs and frameworks (LangChain/LlamaIndex or Strands Agents, similar)

Engineering Excellence & Software Craft

• Write high-quality Python services and libraries with strong software fundamentals

• Apply core CS concepts: data structures, algorithms, concurrency, networking, APIs, and system design

• Create and maintain automated tests (unit/integration), CI/CD pipelines, and code reviews

AWS Cloud-Native Implementation

• Build and deploy services using AWS-native components such as: Lambda, API Gateway, ECS/EKS, S3, DynamoDB/RDS, CloudWatch, IAM, KMS, SQS/SNS, Step Functions

• Implement infrastructure using IaC (Terraform/ CloudFormation/ CDK preferred)

• Support security and compliance patterns (least privilege IAM, encryption, secrets management)

Use Modern Developer Productivity Tools

• Use code-generation and AI-assisted development tools (e.g., GitHub Copilot, Claude Code, IDE copilots) responsibly to improve delivery speed and quality

• Understand limitations, validate generated code, and maintain secure coding practices

Collaboration & Stakeholder Engagement

• Partner with senior engineers, architects, and product owners to refine requirements and deliver iteratively

• Communicate trade-offs, document designs, and contribute to internal enablement (best practices, patterns, reusable components)

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to [email protected] learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy:

Skills and Requirements

• 1-5 years of professional experience in software engineering, ML engineering, or applied AI development

• Strong Python proficiency (clean architecture, packaging, testing, performance basics)

• Solid foundation in Computer Science / Computer Engineering fundamentals: OOP, algorithms/data structures, API design, and debugging

• Experience building and integrating APIs (REST/GraphQL), handling authentication/authorization patterns

• Hands-on experience with AWS and cloud-native development (at least several services in production or near-production)

• Familiarity with LLMs / GenAI concepts and practical usage: Prompting, embeddings, vector search, RAG patterns, evaluation basics

• Comfortable with Git workflows, CI/CD, and modern SDLC practices - Experience with AWS Bedrock or similar managed LLM platforms, or deploying models with SageMaker

• Experience with vector databases/search: OpenSearch, pgvector, Pinecone, Weaviate, FAISS, etc.

• Familiarity with security patterns for GenAI: data redaction, PII handling, prompt injection awareness, content filtering

• Experience with containerization and orchestration: Docker, ECS/EKS, Kubernetes

• Familiarity with observability tooling (metrics/logs/tracing) and performance tuning

• Exposure to MLOps or model evaluation frameworks (e.g., offline test sets, regression suites, quality metrics)

• Experience building internal developer platforms or productivity tools