TECHNICAL AI LEAD

We are hiring a Technical AI Lead with deep expertise in Agentic AI, LLMs, and open-source model ecosystems (Qwen, Llama, Mistral). The role blends hands-on agent development with quality ownership and forward-deployed engineering — building, testing, and hardening agentic systems in real-world, production-like conditions.

Core Skill Areas :

● Forward-Deployed Engineering: Embed on-site with customer teams to adapt and iterate agents

against real workflows, not lab conditions.

● QA & Platform Validation: Structured testing, evaluation frameworks, and production monitoring to catch

failure modes before and after go-live.

● Reliability-First Development: Build multi-agent/LLM pipelines (LangChain, LangGraph) with validation

built into the dev cycle from day one.

● Model & Inference Validation: Benchmark and stress-test open-source models (Qwen, Llama, Mistral)

and inference stacks (vLLM, TGI) for accuracy and stability.

● RAG & Retrieval Validation: Validate embedding and vector search accuracy to ensure grounded

outputs hold up in real use.

• Design, develop, and optimize Agentic AI systems and multi-agent workflows for real-world applications.

• Build customized LLM pipelines using frameworks such as LangChain, LangGraph, and related ecosystems.

• Deploy and optimize open-source LLMs using vLLM, TGI, and Python-based inference stacks.

• Work extensively with open-source models such as Qwen, Llama, Mistral, and similar model families.

• Perform model fine-tuning, evaluation, integration, and inference optimization.

• Work as a Forward-Deployed Engineer, collaborating directly with stakeholders and end users to understand workflows, identify challenges, and rapidly iterate on AI solutions.

• Validate agent and platform performance through structured testing, evaluation frameworks, monitoring, and benchmarking.

• Develop scalable AI services incorporating prompt engineering, autonomous task planning, tool execution, and workflow orchestration.

• Collaborate with engineering, product, and business teams to define AI architecture, performance objectives, and technical best practices.

• Provide technical leadership, mentor team members, and drive engineering standards across AI initiatives.