About the Role :

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, KV cache 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 - and make principled architectural trade-off decisions for client contexts.

- Build reusable accelerators, reference implementations, and evaluation/observability frameworks encoding best practices across engagements.

- Contribute to technical solutioning - architecture designs, proof-of-concepts, and feasibility assessments - in client-facing contexts.

Technical Qualifications :

- 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, including working with vLLM for high-performance LLM serving, optimization, and large-scale inference.

- RAG & Agentic Systems : Experience designing retrieval architectures and building agentic systems using 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, and provider SDK usage across OpenAI, Anthropic, Mistral, Hugging Face, and similar.

- Deployment fundamentals : Proficiency with Docker, containerization, and Linux environments for packaging, deploying, and debugging AI systems.

- Comfortable leveraging AI-assisted tools for collaborative development, code generation, refactoring, and productivity enhancement.

Preferred Qualifications :

- Fine-tuning : Experience with LoRA/QLoRA, dataset curation, and instruction tuning; understanding of when fine-tuning is the right lever vs. prompting or RAG.

- Low-level AI systems : Familiarity with CUDA, Triton, or similar GPU programming models; working knowledge of C++ or Rust.

- Infrastructure & observability : Kubernetes for containerized AI workloads; experience with LangSmith, Arize, W&B, Phoenix, or Prometheus/Grafana for ML observability.

Ways to Stand Out :

- You have built and deployed a production agentic system and can speak to the failure modes and design decisions that only emerge at runtime.

- You have done inference optimization at a systems level - tuning serving infrastructure, implementing custom 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 experience.