Infrastructure Engineer
- India
نُشرت أول أمس
عن الوظيفة
Location: Pune, India
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, 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
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, 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
Fine-tuning: Experience with LoRA/QLoRA, dataset curation, and instruction tuning; understanding of when fine-tuning is the right lever vs. prompting or RAG.EMERGYS
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 From the Crowd
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 experimentation.
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, 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
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, 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
Fine-tuning: Experience with LoRA/QLoRA, dataset curation, and instruction tuning; understanding of when fine-tuning is the right lever vs. prompting or RAG.EMERGYS
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 From the Crowd
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 experimentation.