What are the responsibilities and job description for the Generative AI Engineer position at Net2Source (N2S)?
Title- Senior Software Developer
Location- Plano TX, 3 DAYS/ WEEK, Hybrid, Onsite
We need strong candidates who have developed LLM’s, Designed workflows and AI tools deployment knowledge. This role offers the opportunity to lead in cutting-edge automated software modernization driven by GenAI and platform engineering standards.
Join a horizontal engineering team supporting 600 application teams on a mission to raise engineering maturity by driving standards, guidelines, platform capabilities, and large-scale technical debt remediation. You will build advanced agentic AI workflows to automatically analyze codebases, detect tech debt, and generate high-quality fixes—from vulnerability patches to dependency and language upgrades. This is a hands-on, high-impact role shaping the future of automated software modernization.
Required Skills:
- Deep knowledge of multi-agent architectures including planners, executors, and tool routing.
- Strong understanding of RAG systems: chunking, embeddings, vector/hybrid search, and retrieval policies.
- Experience evaluating LLMs and agent workflows incorporating statistical reasoning and validation.
- Proficiency with AWS (Lambda, ECS/EKS, S3, API Gateway, EC2, IAM) and Infrastructure-as-Code for cloud resource automation and deployment.
- Experience with observability tools (Datadog, logging, tracing, metrics).
- Familiarity with PostgreSQL, DBT, data modeling, schema evolution, and performance tuning.
- Hands on experience on AI
- AI Engineer who has extensive knowledge on LLM creation, AI tool adoption and also defining the frameworks
- should be strongly vocal enough to communicate with the customers
MUST HAVE Qualifications:
Platform runs on AWS and AWS knowledge is must.
• 5 years experience building production-grade systems with end-to-end ownership.
• Expertise in Python programming, software engineering best practices, testing strategies, CI/CD, and system design.
• Hands-on experience shipping LLM-powered features such as autonomous workflows or function calling with measurable impact on reliability or latency.
• Deep knowledge of multi-agent architectures including planners, executors, and tool routing.
• Strong understanding of RAG systems: chunking, embeddings, vector/hybrid search, and retrieval policies.
• Experience evaluating LLMs and agent workflows incorporating statistical reasoning and validation.
• Proficiency with AWS (Lambda, ECS/EKS, S3, API Gateway, EC2, IAM) and Infrastructure-as-Code for cloud resource automation and deployment.
• Experience with observability tools (Datadog, logging, tracing, metrics).
• Familiarity with PostgreSQL, DBT, data modeling, schema evolution, and performance tuning.
• Knowledge of vector databases like Pinecone or pgvector.
• Experience building or optimizing CI/CD pipelines (GitHub Actions or similar).
• Proven track record in application modernization, dependency management, and technical debt reduction.
• Ability to rapidly prototype, validate, and transition solutions to production systems.
Preferred Skills:
• Experience designing agent infrastructure with sandboxing, tool isolation, and fail-safe execution.
• Background in large-scale platform engineering or developer experience tooling.
• Understanding of security, compliance, and privacy for enterprise AI systems.
• Strong architectural communication ability, including RFC writing and diagramming.
Attributes:
• Adaptable and proactive problem solver.
• Strong ownership mindset with excellent collaboration and communication skills.
• Comfortable in ambiguous, fast-paced R&D environments.
• Passionate about building high-leverage platform capabilities impacting hundreds of engineering teams.