Role: AI Lead Developer (GenAI & Agentic Solutions)
Location: Herndon, VA / Grand Prairie, TX/Mobile, Alabama US — Hybrid (minimum 4 days/week onsite is must)
Duration: Contract
Job Description :
We are looking for a profile that combines strong technical skills with effective business communication.
Key expectations include:
- Clear prioritization
- Regular, structured status updates
- Clear ownership of actions
- Stakeholder Management
- The ability to present and explain technical topics in a way that is relatable to the business
- The ability to translate business requirements into AI solutions and project a clear AI vision across relevant use cases
- candidates having strong lead experience who will be handling a team
AI Lead Developer (GenAI & Agentic Solutions)
Role Title: AI Lead Developer (Generative AI / Agentic AI)
Seniority: Senior / Lead (hands-on technical lead)
Role Objective
Drive the design, development, and industrialization of Generative AI and Agentic AI solutions in a global, high-tech enterprise setting. This role owns the journey from PoC → MVP → production with enterprise-grade quality: security, scalability, reliability, observability, and governance. Candidate must be platform-agnostic; hands-on Google Cloud Platform experience is preferred. Familiarity with classical AI/ML for hybrid solutions is a strong advantage.
Key Responsibilities
Technical Leadership & Architecture
- Define solution architecture for GenAI/agentic capabilities (RAG, tool/function calling, orchestration, guardrails).
- Make design decisions balancing quality, latency, cost, and compliance; produce lightweight architecture artifacts and decision logs.
Hands-on Delivery (Prototype to Production)
- Build and deploy production-ready GenAI services/APIs (microservices) and reusable components (accelerators, templates, SDKs).
- Implement data ingestion + retrieval pipelines (chunking, embeddings, indexing) and integrate enterprise data sources.
- Establish evaluation approach (benchmarks, regression tests, golden datasets) and manage prompt/model versioning.
LLMOps / Platform Enablement
- Implement CI/CD, automated testing gates, rollout strategies, monitoring/logging/tracing, and operational runbooks.
- Support incident/change workflows and ensure production readiness (SLOs, resiliency, cost controls).
Security, Privacy & Responsible AI
- Implement controls for PII protection, access management, auditability, prompt-injection mitigation, safety filters, and governance alignment.
Collaboration & Mentoring
- Partner with product, architecture, data, and security stakeholders; translate requirements into backlog and deliverables.
- Mentor engineers and align distributed/global teams on standards and delivery practices.
Required Skills & Experience
- 8+ years software engineering; 2+ years delivering GenAI/LLM solutions (hands-on).
- Demonstrated success taking at least one GenAI solution into production (not only PoCs).
- Strong coding in Python (and/or Java/Go/TypeScript) plus API/service engineering.
- Strong GenAI fundamentals: RAG, embeddings, prompt lifecycle, tool/function calling, agentic patterns, evaluation methods.
- Cloud-native engineering: containers, Kubernetes (or equivalent), CI/CD, IaC, observability.
Preferred / Nice to Have
- Hands-on GCP (e.g., Vertex AI, BigQuery, Cloud Run/GKE, Pub/Sub, IAM/Secret Manager).
- Classical AI/ML exposure for hybrid systems (prediction + GenAI).
- Experience with vector DB / enterprise search and working in regulated/high-security environments.
Deliverables
- Production-grade GenAI/agentic service(s) with monitoring, alerting, runbooks, and support readiness.
- Reference architecture + reusable components and quality gates (evaluation, security, performance, cost).
Secure integration with enterprise data and identity/access controls.