PAM Engineer AI

Lead hands on development of AI enabled and LLM based applications, including agentic and automation driven systems. Design and implement agent orchestration architectures, including task decomposition, multi agent coordination, tool/function invocation, state and memory management, and policy aware execution flows. Engineer robust LLM interaction layers, including prompt design, grounding strategies (e.g., RAG), tool integration, feedback loops, and evaluation mechanisms. Own end to end AI system architecture, spanning APIs, services, data pipelines, model serving, and observability. Ensure AI solutions operate effectively across cloud native and hybrid environments, with attention to scalability, latency, and reliability. Embed security, compliance, and governance by design, including access controls, logging, traceability, explainability, and human in the loop safeguards. Provide technical leadership through architecture ownership, hands on coding, design reviews, and mentorship of AI engineers., Lead Machine Learning Engineer (MLOps, KServe + building Kubernetes Clusters, PyTorch, TensorFlow on AWS) As a Capital One Machine Learning Engineer (MLE), you’ll be part of an Agi…

• 1 day ago +

Proven experience as a hands-on AI or platform engineer with leadership responsibility for production systems. Deep expertise in LLMs, including model selection, prompt engineering, grounding techniques, evaluation, and mitigation of hallucination and drift. Strong experience with agent frameworks and orchestration patterns, including multi agent systems, tool using agents, and agent lifecycle management. Solid background in cloud native architecture, APIs, distributed systems, and modern MLOps/LLMOps practices. Ability to translate business, risk, and regulatory requirements into concrete technical designs and implementations. Experience translating advanced AI (LLMs, agentic workflows, orchestration) into secure, governed, and auditable capabilities that are production ready for large, regulated enterprises. Experience with real-world deployments scenarios ensuring AI solutions work reliably across hybrid environments, integrate with enterprise platforms, and deliver measurable business outcomes. Preferred Qualifications: Experience implementing AI control frameworks (e.g., model controls, guardrails, evaluation, and auditability) aligned to NIST, ISO, or sector regulators. Knowledge of identity, access, and authorization models for agents and non human identities, including least privilege and JIT patterns. Familiarity of frameworks such as OWASP Top 10 for Agentic and LLM Applications, MITRE ATLS and NIST AI RMF.

V R Della Infotech