JD:
Software AI Engineer – Agentic AI
· 10+ years of experience building large-scale distributed systems + strong experience with LLM systems, agentic workflows or advanced ML infrastructure · AI engineers with recent NodeJS/Java script/Typescript experience · Proven ownership of complex, cross-cutting agentic systems spanning multiple teams or products. · Strong engineering fundamentals across backend systems, APIs, data pipelines, and cloud infrastructure. · Deep experience across the agentic AI stack, including planning, tool use, memory, and evaluation. · Fluency with AI-assisted and agentic development workflows. · Comfort operating in ambiguous problem spaces and translating them into shipped, reliable autonomous systems. · Ability to influence technical direction and align teams without formal authority. · Experience in workflow engines, async processing, queues, and streaming systems. · Languages: NodeJS/Java script /Typescript ,Python, Go, · APIs and services: REST, gRPC · Cloud and infrastructure: AWS and/or Google Cloud Platform, Kubernetes · Distributed systems: event-driven architectures, including Kafka · Orchestration Frameworks: Lang Graph, Lang Chain, AirFlow, etc · Integration of commercial and open-source LLMs into agentic workflows
· Agent and orchestration frameworks such as Lang Chain , LlamaIndex, Semantic Kernel, or CrewAI, with strong judgment about when to use frameworks versus building lighter-weight primitives · Model-level work using PyTorch and the Hugging Face ecosystem (embeddings, fine-tuning, inference tooling), with some exposure to TensorFlow · Strong schema, validation, and state management practices using tools such as Pydantic (Python) and Zod (TypeScript)
Roles & Responsibilities
· Drive technical direction for agentic AI initiatives, influencing architecture patterns, autonomy boundaries, and system design. · Design, build, and operate production-grade agentic AI systems used across multiple products. · Own and evolve shared agentic AI capabilities, including: · Agent frameworks and orchestration layers · Planning, tool use, and memory strategies · Retrieval and grounding (RAG) pipelines · LLM infrastructure, inference, and model gateways · Evaluation, observability, and safety tooling for autonomous systems · Lead technical design reviews and help teams navigate tradeoffs involving autonomy, safety, reliability, scalability, and cost. · Partner across teams to deliver complex, cross-cutting agentic AI initiatives from concept to production. · Evaluate emerging models, techniques, and agentic patterns and translate them into practical, enterprise-ready improvements. · Mentor senior engineers and raise the technical bar for agentic AI development through example and influence.
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