Applied AIML Lead-Platform AI Acceleration

Overview

In this hands-on leadership role, you design, build, and operate production-grade ML and Generative AI services that scale across multiple streams. You set technical direction, ensure security and reliability, and mentor engineers to raise delivery quality. You collaborate with product teams and data scientists to translate requirements into robust AI-enabled systems for enterprise environments. The role blends code, architecture, and governance to advance enterprise AI adoption and innovation.

Responsibilities

• Design, develop, and deploy ML/LLM/GenAI solutions from concept to production with reliability ownership
• Collaborate with product managers, data scientists, ML engineers, and stakeholders to prioritize use cases
• Develop secure, testable services and libraries integrating LLMs, tools, RAG, and agentic workflows
• Build end-to-end RAG/Agentic RAG pipelines (chunking/indexing, retrieval tuning, re-ranking, grounding checks)
• Optimize fine-tuning for specific NLP use cases to ensure high-quality outputs
• Mentor junior engineers through design/code reviews, pairing, and coaching
• Implement real-time monitoring to ensure AI solution reliability and compliance
• Communicate AI/ML capabilities and results to technical and non-technical audiences
• Stay updated with AI/ML/LLM/GenAI advancements and leverage external APIs for enhanced functionality

Key requirements

• Proven delivery of LLM-enabled applications using agentic patterns (tool use, orchestration, guardrails, structured outputs)
• Hands-on experience building and operating MCP integrations in production
• Experience delivering production data-driven software/tools in secure, regulated environments
• Expertise in Python engineering (production-grade design, testing, debugging, performance tuning)
• Advanced prompt engineering (system prompts, few-shot, tool/function calling, schema-constrained outputs)
• Understanding of agentic AI system layers (context management, harness design, loop engineering)
• Experience building conversational AI solutions (RAG, Agentic, Graph RAG)
• Experience scaling AI/ML workloads with distributed training/serving (e.g., Ray) and GPU acceleration (CUDA)
• Proficiency with modern AI architectures (RAG, multi-agent systems)
• Familiarity with LLM evaluation (quality, safety, reliability; guardrails, content filtering)
• Proficiency in GenAI/agentic AI engineering practices (data sensitivity, secure inputs/outputs, resiliency and security)
• Successful adoption of enterprise-approved AI-assisted engineering tools

• Collaborative mindset
• Mentoring and coaching
• Strong communication across technical and non-technical audiences
• Python production-grade development
• LLM/GenAI engineering
• Agentic AI workflows and tool use

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