About the Role
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.