Back
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Metro location and mid-level experience increase applicants, but niche LLM deployment skills reduce competition.
Medium — applied AI deployment skills transfer across industries but enterprise system integration creates moderate domain bias.
Medium — explicit 2–5 years, Python and LLM stack requirements, and enterprise integration experience filter candidates.
Deliver custom, production-ready AI solutions by partnering with enterprise customers and leading the full lifecycle from discovery to launch.
Design, deploy, tune, and troubleshoot AI agents integrated into complex customer workflows delivering measurable business outcomes (e.g., cost reduction, efficiency improvements).
Collaborate cross-functionally to incorporate customer feedback into product roadmaps and ensure stable handoff of deployments to engineering or support teams.
2–5 years experience in software engineering, solutions engineering, or ML deployment roles.
Proficiency in Python and working knowledge of AI/ML frameworks such as OpenAI, Hugging Face, LangChain, Pinecone, or Weaviate.
Experience building applications with LLMs, RAG systems, or conversational AI workflows, and integrating enterprise systems via APIs or middleware (e.g., Salesforce, ServiceNow, SAP, Oracle).
Work Experience Required: 2–5 years explicitly mentioned; Notice Period: Not explicitly mentioned in the JD.
Experienced in deploying AI solutions in enterprise or mission-critical environments, particularly within SaaS, customer experience, or manufacturing contexts.
Capable of translating complex business needs into technical AI implementations rapidly while maintaining ownership from design through tuning and launch.
Comfortable engaging directly with customers and internal teams, balancing technical depth with interpersonal collaboration to drive adoption and feedback integration.