ML Systems Engineer (Agentic AI & Inference Engineering)

OVERVIEW We are hiring an ML Systems Engineer to design and deliver cutting-edge AI solutions for enterprise clients at the frontier of agentic AI, inference engineering, and ML systems architecture. You will go beyond applied ML - dissecting how AI systems are built, optimized, and scaled - designing production- grade architectures spanning retrieval systems, inference pipelines, and agentic workflows. You will translate state-of-the-art capabilities into robust, performant solutions, operating at the intersection of ML research awareness and engineering discipline. KEY RESPONSIBILITIES Design and deliver production-grade AI systems for enterprise clients spanning agentic workflows,LLM inference pipelines, and retrieval-augmented architectures. Lead ML systems architecture decisions - model serving topology, inference backend selection, KVcache management, batching strategies, and memory optimization - alongside ML performance engineering to profile bottlenecks, benchmark throughput/latency, and evaluate quantization strategies (GPTQ, AWQ, GGUF). Architect RAG pipelines and agentic AI systems - from chunking, embedding, hybrid retrieval, and re-ranking through to multi-agent orchestration, tool use, and memory architectures. Evaluate frontier model capabilities - reasoning models, multimodal systems, fine-tuned variants - andmake principled architectural trade-off decisions for client contexts. Build reusable accelerators, reference implementations, and evaluation/observability frameworksencoding best practices across engagements. Contribute to technical solutioning - architecture designs, proof-of-concepts, and feasibilityassessments - in client-facing contexts. TECHNICAL QUALIFICATIONS Core Requirements Python & ML ecosystem: Strong programming skills with production AI system experience; hands-on with the PyTorch ecosystem including Hugging Face Transformers, PEFT, Accelerate, and Datasets. LLM inference & serving: Deep knowledge of KV cache mechanics, quantization, and batching;hands-on with at least one inference runtime (vLLM, TGI, TensorRT-LLM, SGLang, or similar). Hands-on experience supporting AI/ML and LLM inference platforms at scale, includingworking with vLLM for high-performance LLM serving, optimization, and large-scale inference. RAG & Agentic Systems: Experience designing retrieval architectures and building agentic systemsusing LangGraph, LlamaIndex Workflows, AutoGen, or CrewAI - including tool use, memory, and multi-agent coordination. LLM APIs & prompt engineering: Strong grasp of structured output generation, function calling, andprovider SDK usage across OpenAI, Anthropic, Mistral, Hugging Face, and similar. Deployment fundamentals: Proficiency with Docker, containerization, and Linux environments forpackaging, deploying, and debugging AI systems. Comfortable leveraging AI-assisted tools for collaborative development, code generation, refactoring,and productivity enhancement. Preferred Fine-tuning: Experience with LoRA/QLoRA, dataset curation, and instruction tuning; understandingof when fine-tuning is the right lever vs. prompting or RAG. Low-level AI systems: Familiarity with CUDA, Triton, or similar GPU programming models; workingknowledge of C++ or Rust. Infrastructure & observability: Kubernetes for containerized AI workloads; experience withLangSmith, Arize, W&B, Phoenix, or Prometheus/Grafana for ML observability. WAYS TO STAND OUT FROM THE CROWD You have built and deployed a production agentic system and can speak to the failure modes anddesign decisions that only emerge at runtime. You have done inference optimization at a systems level - tuning serving infrastructure, implementingcustom batching logic, or optimizing a quantization pipeline to hit real SLAs. You have open-source contributions to prominent ML systems repositories - vLLM, SGLang,llama.cpp, TGI, LangChain, LlamaIndex, or similar - demonstrating work that holds up to community scrutiny. You have designed custom LLM evaluation frameworks with structured regression harnesses,domain-specific evals, or human-in-the-loop feedback loops - beyond off-the-shelf metrics. You bring a client-facing engineering mindset and can defend opinions on reasoning models, long-context retrieval, or inference hardware tradeoffs based on hands-on experimentation. Compensation: 500,000.00 - 1,800,000.00 per year Benefits: Provident FundWork Location: In person OVERVIEW We are hiring an ML Systems Engineer to design and deliver cutting-edge AI solutions for enterprise clients at the frontier of agentic AI, inference engineering, and ML systems architecture. You will go beyond applied ML - dissecting how AI systems are built, optimized, and scaled - designing production- grade architectures spanning retrieval systems, inference pipelines, and agentic workflows. You will translate state-of-the-art capabilities into robust, performant solutions, operating at the intersection of ML research aw