Lead the development of AI applications that integrate LLMs with APIs, databases, and enterprise systems.
Oversee the design and optimization of AI agent & pipelines to enhance factuality.
Build scalable agentic workflows and orchestration systems for enterprise use cases.
Own the architecture and design of applied AI systems (bots, RAG, agents, integrations, MCP servers).
Define patterns, frameworks, and reusable components to accelerate AI solution delivery across teams.
Drive evaluation and guardrail frameworks for ensuring AI reliability, safety, and performance.
Partner with product managers and stakeholders to translate ambiguous business needs into technical solutions.
Mentor engineers, review designs/code, and guide teams in applied AI best practices.
Collaborate with security, compliance, and data teams to ensure responsible AI adoption.
Stay ahead of emerging LLM frameworks, orchestration tools, and AI agents.
Identify opportunities for new AI-powered capabilities within existing systems.
Contribute to long-term AI roadmap and platform strategy.
Requirements
Strong experience in Python, TypeScript/JavaScript, and distributed systems.
Hands-on experience with LLM APIs (OpenAI, Anthropic, etc.), orchestration frameworks (LangChain, LlamaIndex, CrewAI, LangGraph), and vector databases (Elasticsearch, Pinecone, etc.).
Proven track record of building production-grade AI applications (bots, agents, RAG systems).
8+ years of software engineering experience with 2+ in applied AI/ML.
Prior experience as a tech lead or staff-level engineer, influencing technical direction beyond a single team.
Strong ability to communicate AI concepts to both technical and non-technical stakeholders.
Exceptional ability to abstract problems into AI-powered workflows.
Skilled at balancing rapid prototyping with building for scale and reliability.
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
PythonTypeScriptJavaScriptdistributed systemsLLM APIsorchestration frameworksvector databasesproduction-grade AI applicationsapplied AImachine learning
Soft skills
communicationmentoringproblem abstractioncollaborationinfluencing technical directionstakeholder engagementdesign reviewguidance in best practicesbalancing prototyping and reliabilityteam leadership