AI Engineer (Lead) - Only W2

Role: AI Engineer (Lead) Remote (US) Job Type: W2 Contract Length: 6 months with extension Visa Independent canddiates are preferred (Only W2) - No C2C Description • Client is seeking an experienced AI Engineer to drive the transformation of AI initiatives into production-ready, enterprise-scale solutions. • This role will focus on Agentic AI systems and will work closely with client's AI, engineering and product teams. Responsibilities • Design, build, and deploy full‑stack applications using current stack (Python + React) while remaining language-agnostic. • Architect secure, scalable systems with a strong emphasis on security, compliance, and best practices. • Lead and contribute to internal AI initiatives, including: • Supporting existing AI-enabled products. • Designing and implementing new AI-driven solutions. • Providing internal consulting on how teams can leverage AI. • Use and evaluate a diverse set of AI developer tools (GitHub Copilot, Cursor, Claude). • Serve as a technical thought partner to business units to identify opportunities where AI can improve efficiency, reduce risk, or enhance customer experience. • Ensure all solutions meet security, compliance, and regulatory expectations, particularly in complex environments such as financial services. • Build and integrate agentic systems powered by cutting-edge LLM and GenAI technologies. • Work closely with AI Engineers to turn AI capabilities into production-ready enterprise solutions. • Design, develop, and deploy agentic AI systems leveraging LLMs and modern AI frameworks. • Integrate GenAI models into full-stack applications and internal workflows. • Collaborate on prompt engineering, fine-tuning, and evaluation of generative outputs. • Optimize AI inference pipelines for scalability, latency, and cost efficiency. Required Skills & Qualifications • 7+ years of full stack development experience using programming languages like Python, JavaScript, Node.js, ReactJS. • Experience with LLM frameworks, (LangChain, Bedrock Data Automation). • Understanding of Git, CI/CD, DevOps, and production-grade GenAI deployment practices. • Working experience in Data Processing, AI-enabled workflows using Python. • Knowledge of LLM, Prompt Engineering, RAG Architecture, Agentic AI. • Deep understanding of LLMs, embeddings, vector databases. • Experience with Docker, Kubernetes, and cloud-native deployment practices. • Knowledge of AI observability, model monitoring, and cost optimization strategies. Nice To Have • Experience in financial services, especially with core banking systems or other highly complex, regulated domains. • Deep familiarity with modern AI development patterns, prompt engineering, vector databases, embeddings, or retrieval pipelines. • Experience with LLM orchestration, prompt management, and evaluation frameworks. • Knowledge of data governance, security, and compliance in enterprise environments.